CA2144068A1 - Fraud detection using predictive modeling - Google Patents
Fraud detection using predictive modelingInfo
- Publication number
- CA2144068A1 CA2144068A1 CA002144068A CA2144068A CA2144068A1 CA 2144068 A1 CA2144068 A1 CA 2144068A1 CA 002144068 A CA002144068 A CA 002144068A CA 2144068 A CA2144068 A CA 2144068A CA 2144068 A1 CA2144068 A1 CA 2144068A1
- Authority
- CA
- Canada
- Prior art keywords
- fraud
- data
- computer
- profile
- customer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F7/00—Mechanisms actuated by objects other than coins to free or to actuate vending, hiring, coin or paper currency dispensing or refunding apparatus
- G07F7/08—Mechanisms actuated by objects other than coins to free or to actuate vending, hiring, coin or paper currency dispensing or refunding apparatus by coded identity card or credit card or other personal identification means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/403—Solvency checks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M15/00—Arrangements for metering, time-control or time indication ; Metering, charging or billing arrangements for voice wireline or wireless communications, e.g. VoIP
- H04M15/47—Fraud detection or prevention means
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M2215/00—Metering arrangements; Time controlling arrangements; Time indicating arrangements
- H04M2215/01—Details of billing arrangements
- H04M2215/0148—Fraud detection or prevention means
Abstract
An automated system and method detects fraudulent transac-tions using a predictive model such as a neural network to evaluate indiviual customer accounts and identify potentially fraudulent transactions based on learned relationships among known variables.
The system may also output reason codes indicating relative contrib-utions of various variables to a particular result. The system periodi-cally monitors its performance and redevelops the model when per-formance drops below a predetermined level.
The system may also output reason codes indicating relative contrib-utions of various variables to a particular result. The system periodi-cally monitors its performance and redevelops the model when per-formance drops below a predetermined level.
Description
WO 94/06103 ~ ~ ~ 4 ~ ~ 8 PCr/USs3/08400 _ FRAUD DETECTION USING PREDICTIVE MODELING
CROSS-REFERENCE TO RELATED APPLICATION
.
The subject matter of this application is related to the subject matter of pending U.S. ap-plication Serial No. 07/814,179, (attorney's docket number 726) for "Neural Network Having Expert System Functionality", by Curt A. Levey, filed December 30, 1991, the disclosure of which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
1. Field of the Invention This invention relates generally to the detection of fraudulent use of customer accounts and account numbers, including for example credit card transactions. In particular, the invention relates to an automated fraud detection system and method that uses predictive modeling to perform pattern recognition and classification in order to isolate transactions having high probabilities of fraud.
CROSS-REFERENCE TO RELATED APPLICATION
.
The subject matter of this application is related to the subject matter of pending U.S. ap-plication Serial No. 07/814,179, (attorney's docket number 726) for "Neural Network Having Expert System Functionality", by Curt A. Levey, filed December 30, 1991, the disclosure of which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
1. Field of the Invention This invention relates generally to the detection of fraudulent use of customer accounts and account numbers, including for example credit card transactions. In particular, the invention relates to an automated fraud detection system and method that uses predictive modeling to perform pattern recognition and classification in order to isolate transactions having high probabilities of fraud.
2. Description of the Related Art In the following discussion, the term "credit card" will be used for illustrative purposes;
however, the techniques and principles discussed herein apply to other types of customer accounts, such as charge cards, bank automated teller machine cards and telephone calling cards.
Credit card issuers conventionally attempt to limit fraud losses by immediately closing a customer's account upon receiving a report that the card has been lost or stolen. Typically, the customer's credit information is then transferred to a new account and a new card is issued. This procedure is only effective in limiting fraudulent use of lost or stolen cards after the loss or theft has been reported to the issuer.
In many cases, however, fraudulent use occurs without the knowledge of the cardholder, and therefore no report is made to the issuer. This may occur if the customer is unaware that the 3 PCI~US93/08400 ~14~Q68 -2-card has been lost or stolen, or if other techniques are employed to perpetrate the fraud, such as:
use of counterfeit cards; merchant fraud; application fraud; or interception of credit cards in the mail. In all these situations, the fraudulent use may not be detected until (and unless) the card-holder notices an unf~mili~r transaction on his or her next monthly statement and contests the corresponding charge. The concomitant delay in detection of fraud may result in .~ignific~nt losses. User fraud, in which the user claims that a valid transaction is invalid, is also possible.
Issuers of credit cards have sought to limit fraud losses by attempting to detect fraudulent use before the cardholder has reported a lost or stolen card. One conventional technique is known as parameter analysis. A parameter analysis fraud detection scheme makes a decision using a small number of database fields combined in a simple Boolean condition. An example of such a condition is:
if (number of transactions in 24 hours > X) and (more than Y dollars authorized)then flag this account as high risk Parameter analysis will provide the values of X and Y that satisfy either the required detection rate or the required false positive rate. In a hypothetical example, parameter values of X=400 and Y=l000 might capture 20% of the frauds with a false positive rate of 200:1, while X=6 and Y=2000 might capture 8% of the frauds with a false positive rate of 20: l .
The rules that parameter analysis provides are easily implemented in a database management system, as they are restricted to Boolean (e.g., and, or) combinations of conditions on single variables.
Parameter analysis derives rules by e~mining the single variables most able to distinguish fraudulent from non-fraudulent behavior. Since only single-variable threshold comparisons are used, complex interactions among variables are not captured. This is a limitation that could cause the system to discrimin~te poorly between fr~1ld1l1ent and valid account behavior, resulting in low capture rates and high false-positive rates.
Additionally, an effective fraud detection model generally requires more variables than conventional parameter analysis systems can handle. Furthermore, in order to capture new fraud schemes, parameter analysis systems must be redeveloped often, and automated redevelopment is difficult to implement.
It is desirable, therefore, to have an automated system that uses available information regarding cardholders, merchants, and transactions to screen transactions and isolate those which are likely to be fraudulent, and which captures a relatively high proportion of frauds while m~int~ining a relatively low false-positive rate. Preferably, such a system should be able to handle a large number of interdependent variables, and should have capability for redevelopment of the underlying system model as new patterns of fraudulent behavior emerge.
-SUMMARY OF THE INVENTION
In accordance with the present invention, there is provided an automated system and method for detecting fraudulent transactions, which uses a predictive model such as a neural network to evaluate individual customer accounts and identify potentially fraudulent transactions based on learned relationships among known variables. These relationships enable the system to estimate a probability of fraud for each transaction. This probability may then be provided as output to a human decision-maker involved in processing the transaction, or the issuer may be signaled when the probability exceeds a predetermined amount. The system may also output reason codes that reveal the relative contributions of various factors to a particular result. Fi-nally, the system periodically monitors its performance, and redevelops the model when performance drops below a predetermined level.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a block diagram of an implementation of the present invention.
Figure 2 is a sample system monitor screen which forms part of a typical output interface for the present invention.
Figure 3 is a sample account selection screen which forms part of a typical output interface for the present invention.
Figure 4 is a sample transaction analysis screen which forms part of a typical output interface for the present invention.
Figure 5 is a sample customer information screen which forms part of a typical output interface for the present invention.
Figure 6 is a sample analyst response screen which forms part of a typical output interface for the present invention.
Figure 7 is a flowchart illustrating the major functions and operation of the present invention.
Figure 8 is a block diagram showing the overall functional architecture of the present invention.
Figure 9 is a diagram of a single proces.~ing element within a neural network.
Figure 10 is a diagram illustrating hidden processing elements in a neural network.
Figure 11 is a flowchart of the pre-processing method of the present invention.
Figure 12 is a flowchart of the method of creating a profile record of the present invention.
Figure 13 is a flowchart of the method of updating a profile record of the present invention.
WO 94/06103 i- PCI/US93/08400 2144Q68 ~4~
Figure 14 is a flowchart showing operation of a batch transaction procescing system according to the present invention.
Figure 15 is a flowchart showing operation of a semi-real-time transaction processing system according to the present invention.
Figure 16 is a flowchart showing operation of a real-time processing system according to the present invention.
Figure 17 is a flowchart showing the overall operation of the transaction proces.cing component of the present invention.
Figure 18 is a flowchart showing the operation of module CSCORE of the present invention.
Figure 19 is a flowchart showing the operation of DeployNet of the present invention.
Figure 20 is a flowchart showing c~cc~d~d operation of the present invention.
Figure 21 is a portion of a typical CFG model definition file.
DESCRIPTION OF THE PREFERRED EMBODIMENT
The Figures depict preferred embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.
Referring now to Figure 1, there is shown a block diagram of a typical implementation of a system 100 in accordance with the present invention. Transaction information is applied to system 100 via data network 105, which is connected to a conventional financial data facility 106 collecting transaction information from conventional sources such as human-operated credit-card authorization termin~l.c and automated teller machines (not shown). CPU 101 runs software program instructions, stored in program storage 107, which direct CPU 101 to perform the various functions of the system. In the preferred embodiment, the software program is written in the ANSI C language, which may be run on a variety of conventional haldwale platforms. In accordance with the software program instructions, CPU 101 stores the data ob-tained from data network 105 in data storage 103, and uses RAM 102 in a conventional manner as a workspace. CPU 101, data storage 103, and program storage 107 operate together to provide a neural network model 108 for predicting fraud. After neural network 108 processes the information, as described below, to obtain an indication of the likelihood of fraud, a signal indicative of that likelihood is sent from CPU 101 to output device 104.
In the preferred embodiment, CPU 101 is a Model 3090 IBM mainframe computer, RAM 102 and data storage 103 are conventional RAM, ROM and disk storage devices for the WO 94/06103 2 ~ 4 4 ~ 6 8 PCI/US93/08400 Model 3090 CPU, and output device 104 is a conventional means for either printing results based on the signals generated by neural network 108, or displaying the results on a video screen using a window-based interface system, or sending the results to a database for later access, or sending a signal dependent on the results to an authorization system (not shown) for further pro-cec~ing.
Referring now also to Figures 2 through 6, there are shown sample screens from aconventional window-based interface system (not shown) which forms part of output device 104.
Figure 2 shows system monitor 201 that allows a fraud analyst or system supervisor to review system performance. System monitor 201 shows a cutoff score 202 above which accounts will be flagged, the number of accounts with scores above the cutoff 203, and the fraud score 204 and account number 205 for a particular account.
Figure 3 shows account selection screen 301 that includes a scrolling window 302allowing the analyst to select high-risk transactions for review, and a set of buttons 303 allowing the analyst to select further operations in connection with the selected transactions.
Figure 4 shows transaction analysis screen 401 that allows the fraud analyst to examine each high-risk transaction and determine applop.iate fraud control actions. It includes account information 402, fraud score 403, explanations derived from reason codes 404 that indicate the reasons for fraud score 403, and two scrolling windows 405 and 406 that show transaction in-formation for the current day and the past seven days 405, and for the past six months 406.
Figure 5 shows customer information screen 501 that allows the analyst to accesscustomer information, including account number 502, customer names 503, best time to call 504, phone numbers 505, and address 506. It also provides access to further functions via on-screen buttons 507.
Figure 6 shows analyst response screen 601 that allows the analyst to log actions taken to control fraud. It includes a series of check boxes 602 for logging information, a comment box 603, and on-screen buttons 604 allowing access to other functions.
Referring now also to Figure 7, there is shown an overall flowchart illustrating the major functions and operation of the system 100. First neural network model 108 is trained 701 using data describing past transactions from data network 105. Then data describing the network model are stored 702. Once the model description is stored, system 100 is able to process current transactions. System 100 obtains data for a current transaction 703, and applies the - current transaction data to the stored network model 704. The model 108 determines a fraud score and reason codes (described below), which are output 705 to the user, or to a database, or to another system via output device 104.
Referring now to Figure 8, the overall functional architecture of system 100 is shown.
System 100 is broken down into two major components: model development component 801 and transaction processing component 802. Model development component 801 uses past data WO 94/06103 PCr/US93/08400 21~4068 -6- --804 to build neural network 108 cont~ining information representing learned relationships among a number of variables. Together, the learned relationships form a model of the behavior of the variables. Although a neural network is used in the preferred embodiment, any type of predictive modeling technique may be used. For purposes of illustration, the invention is described here in terms of a neural network.
Transaction proces.sing component 802 performs three functions: 1) it determines the likelihood of fraud for each transaction by feeding data from various sources 805, 806 into neural network 108, obtaining results, and outputting them 807; 2) when applicable, it creates a record in a profile database 806 summarizing past transactional patterns of the customer; and 3) when applicable, it updates the applol),iate record in profile database 806.
Each of the two components of the system will be described in turn.
Model Development Component 801 Neural Networks: Neural networks employ a technique of "learning" relationships through repeated exposure to data and adjustment of intern~l weights. They allow rapid model development and automated data analysis. Essentially, such networks represent a statistical modeling technique that is capable of building models from data cont~ining both linear and non-linear relationships. While sirnilar in concept to regression analysis, neural networks are able to capture nonline~rity and interactions among independent variables without pre-specification. In other words, while traditional regression analysis requires that nonline~rities and interactions be detected and specified m~nll~lly, neural networks perform these tasks autom~tic~lly. For a more detailed description of neural networks, see D.E. Rumelhart et al, "Learning Representations by Back-Propagating Errors", Nature v. 323, pp. 533-36 (1986), and R. Hecht-Nielsen, "Theory of the Backpropagation Neural Network", in Neural Networks for Perception. pp. 65-93 (1992), the teachings of which are incorporated herein by reference.
Neural networks comprise a number of interconnected neuron-like processing elements that send data to each other along connections. The strengths of the connections among the pro-cessing elements are represented by weights. Referring now to Figure 9, there is shown a diagram of a single processing element 901. The processing element receives inputs Xl, X2, ...
Xn, either from other proces~ing elements or directly from inputs to the system. It multiplies each of its inputs by a corresponding weight wl, w2, ... Wn and adds the results together to form a weighted sum 902. It then applies a transfer function 903 (which is typically non-linear) to the weighted sum, to obtain a value Z known as the state of the element. The state Z is then either passed on to another element along a weighted connection, or provided as an output signal.
Collectively, states are used to represent information in the short term, while weights represent long-term information or learning.
Processing elements in a neural network can be grouped into three categories: input Wo 94/06103 2 ~ 4 4 0 ~ PCr/USs3/08400 processing elements (those which receive input data values); output procescing elements (those which produce output values); and hidden processing elements (all others). The purpose of hidden processing elements is to allow the neural network to build intermediate representations that combine input data in ways that help the model learn the desired mapping with greater accuracy. Referring now to Figure 10, there is shown a diagram illustrating the concept of hidden processing elements. Inputs 1001 are supplied to a layer of input procescin~ elements 1002. The outputs of the input elements are passed to a layer of hidden elements 1003.
Typically there are several such layers of hidden elements. Eventually, hidden elements pass outputs to a layer of output elements 1004, and the output elements produce output values 1005.
Neural networks learn from examples by modifying their weights. The "training"
process, the general techniques of which are well known in the art, involves the following steps:
1) Repeatedly presenting examples of a particular inputloutput task to the neural network model;
2) Comparing the model output and desired output to measure error; and 3) Modifying model weights to reduce the error.
This set of steps is repeated until further iteration fails to decrease the error. Then, the network is said to be "trained." Once training is completed, the network can predict outcomes for new data inputs.
Fraud-Related Variables In the present invention, data used to train the model are drawn from various database files containing historical data on individual transactions, merchants, and customers. These data are preferably pre-processed before being fed into the neural network, resulting in the creation of a set of fraud-related variables that have been empirically deterrnined to form more effective predictors of fraud than the original historical data.
Referring now to Figure 11, there is shown a flowchart of the pre-processing method of the present invention. Individual elements of the flowchart are indicated by designations which correspond to module names.
Data used for pre-processing is taken from three databases which contain past data: 1) past transaction database 1101 (also called an "authorization database") cont~ining two years' worth of past transaction data, which may be implementecl in the same data base as past data 804; 2) customer database 1103 cont~ining customer data; and 3) fraud database 1102 which indicates which accounts had fraudulent activity and when the fraudulent activity occurred.
Module re~ th c~c 1104 reads transaction data from past transaction database 1101.
Module match~llth c:lc 1105 samples this transaction data to obtain a new transaction data set containing all of the fraud accounts and a randomly-selected subset of the non-fraud accounts.
In creating the new transaction data set, module match~nth s~c 1105 uses information from fraud database 1102 to determine which accounts have fraud and which do not. For effective network training, it has been found preferable to obtain approximately ten non-fraud accounts for every fraud account.
Module readex.sas 1106 reads customer data from customer database 1103. Module matchex.sas 1107 samples this customer data to obtain a new customer data set cont~ining all of the fraud accounts and the same subset of non-fraud accounts as was obtained by module match~--th ~c In creating the new customer data set, module matchex.sas 1107 uses informa-tion from fraud database 1102 to determine which accounts have fraud and which do not.
Module mxmerge.sas 1108 merges all of the data sets obtained by modules match~llth ~ 1105 and matchex.sas 1107. Module genau.sas 1109 subdivides the merged data set into subsets of monthly data.
Module gensamp.sas 1112 samples the data set created by module mxmerge.sas 1108 and subdivided by genau.sas 1109, and creates a new data set called sample.ssd where each record represents a particular account on a particular day with transaction activity. Module gensamp.sas 1112 determines which records are fraudulent using information from fraud database 1102. Module gensamp.sas 1112 provides a subset of authorization days, as follows:
From the database of all transactions, a set of active account-days is created by removing multiple transactions for the same customer on the same day. In the set of active account-days, each account day is assigned a "draft number" from 0 to 1. This draft number is assigned as follows: If the account-day is non-fraudulent, then the draft number is set to a random number between 0 and 1. If the account-day is fraudulent and it lies on the first or second day of fraud, then the draft number is set to 0. Otherwise, it is set to 1. Then, the 25,000 account-days with the smallest draft numbers are selected for inclusion in sample.ssd. Thus, all fraudulent account-days (up to 25,000) plus a sample of non-fraudulent account-days are included in sample.ssd.
Module rolll5.sas 1113 generates a lS-day rolling window of data. This data has multiple records for each account-day listed in sample.ssd. The current day and 14 preceding days are listed for each sample account.
Module rolll5to7.sas 1117 takes the rolll5 data set and filters out days eight to 15 to produce roll7, a 7-day rolling window data set 1119. Days eight to 15 are ignored. Module genrolv.sas 1118 generates input variables for a rolling window of the previous 15 days of transactions. It processes a data set with multiple and variable numbers of records per account and produces a data set with one record per account. The result is called rollv.ssd.
Module rolll5tol.sas 1114 takes the rolllS data set and filters out days except the current day to produce rolll . Module gencurv.sas 1115 uses roll 1 to generate current day variables 1116 describing transactions occurring during the current day.
Module genprof.sas generates profile variables which form the profile records 1111.
2~44~68 Wo 94/06103 PCI/US93/08400 g Module merge.sas 1120 combines the profile records 1111, 1-day variables 1116, and 7-day variables 1119 and generates new fraud-related variables, as listed below, from the combina-tion. It also merges rollv.ssd with the sample-filtered profile data sets to produce a single data set with both profile and rolling window variables. The result is called the modln2 data set 1121 (also called the "training set"), which contains the fraud-related variables needed to train the net-work. Scaler module 1122 scales the variables such that the mean value for each variable in the scaled training set is 0.0 and the standard deviation is 1.0, to create scaled modln2 data set 1123.
Many fraud-related variables may be generated using variations of the pre-processing method described above. Fraud-related variables used in the preferred embodiment include:
Customer usage pattern profiles representing time-of-day and day-of-week profiles;
Expiration date for the credit card;
Dollar amount spent in each SIC (Standard Tn~lllstri~l Classification) merchant group category during the current day;
Percentage of dollars spent by a customer in each SIC merchant group category during the current day;
Number of transactions in each SIC merchant group category during the current day;
Percentage of number of transactions in each SIC merchant group category during the current day;
Categorization of SIC merchant group categories by fraud rate (high, medium, - or low risk);
Categorization of SIC merchant group categories by customer types (groups of customers that most frequently use certain SIC categories);
Categorization of geographic regions by fraud rate (high, medium, or low risk);
Categorization of geographic regions by customer types;
Mean number of days between transactions;
Variance of number of days between transactions;
Mean time between transactions in one day;
Variance of time between transactions in one day;
Number of multiple transaction declines at same merchant;
Number of out-of-state transactions;
Mean number of transaction declines;
Year-to-date high balance;
Transaction amount;
Transaction date and time;
Transaction type.
WO 94/06103 ~ PCr/US93/08400 21~ g -10- --Additional fraud-related variables which may also be considered are listed below:
Current Day Cardholder Fraud Related Variables bweekend - current day boolean in~ ting current datetime considered weekend cavapvdl - current day mean dollar amount for an a~plov~l cavapvdl - current day mean dollar amount for an approval cavaudl - current day mean dollars per auth across day ccoscdom - current day cosine of the day of month i.e. cos(day ((datepart(cst_dt) *
&TWOPI)/30));
ccoscdow - current day cosine of the day of week i.e. cos(weekday((datepart(cst_dt) *
&TWOPI)/7));
ccoscmoy - current day cosine of the month of year i.e. cos(month ((datepart(cst_dt) *
&TWOPI)/12));
cdom - current day day of month cdow - current day day of week chdzip - current cardholder zip chibal - current day high balance chidcapv - current day highest dollar amt on a single cash approve chidcdec - current day highest dollar amt on a single cash decline chidmapv - current day highest dollar amt on a single merch approve chidmdec - current day highest dollar amt on a single merch decline chidsapv - current day highest dollar amount on a single approve chidsau - current day highest dollar amount on a single auth chidsdec - current day highest dollar amount on a single decline cmoy - current day month of year cratdcau - current day ratio of declines to auths csincdom - current day sine of the day of month i.e. sin(day ((datepart(cst_dt) * &TWOPI)/30));
csincdow - current day sine of the day of week i.e. sin(weekday((datepart(cst_dt) *
&TWOPI)/7));
csincmoy - current day sine of the month of year i.e. sin(month ((datepart(cst_dt) *
&TWOPI)/12));
cst_dt - current day cst datetime derived from zip code and CST auth time ctdapv - current day total dollars of approvals ctdau - current day total dollars of auths ctdcsapv - current day total dollars of cash advance approvals ctdcsdec - current day total dollars of cash advance declines WO 94/06103 ~! t4 4 ~6 ~ PCI/US93/08400 ctddec - current day total dollars of declines ctdmrapv - current day total dollars of merch~n~i~e approvals ctdmrdec - current day total dollars of merch~n(li~e declines ctnapv - current day total number of approves ctnau - current day total number of auths ctnaulOd - current day number of auths in day<=$10 ctnaudy - current day total number of auths in a day ctncsapv - current day total number of cash advance approvals ctncsapv - current day total number of cash approves ctncsdec - current day total number of cash advance declines ctndec - current day total number of declines c~ - current day total number of merchandise approvals ctnmrdec - current day total number of merchandise declines ctnsdapv - current day total number of approvals on the same day of week as current day ctnwdaft - current day total number of weekday afternoon approvals ctnwdapv - current day total number of weekday approvals ctnwdeve - current day total number of weekday evening approvals ctnwdmor - current day total number of weekday morning approvals ctnwdnit - current day total number of weekday night approvals ctnweaft - current day total number of weekend afternoon approvals ctnweapv current day total number of weekend approvals ctnweeve current day total number of weekend evening approvals ctnwemor current day total number of weekend morning approvals ctnwenit current day total number of weekend night approvals currbal current day current balance cvr:mdl current day variance of dollars per auth across day czrate 1 current day zip risk group 1 'Zip very high fraud rate' czrate2 current day zip risk group 2 'Zip high fraud rate' czrate3 current day zip risk group 3 'Zip medium high fraud rate' czrate4 current day zip risk group 4 'Zip medium fraud rate' czrateS current day zip risk group S 'Zip medium low fraud rate' czrate6 current day zip risk group 6 'Zip low fraud rate' czrate7 current day zip risk group 7 'Zip very low fraud rate' czrate8 current day zip risk group 8 'Zip unknown fraud rate' ctdsfaO 1 current day total dollars of transactions in SIC factor group 01 ctdsfaO2 current day total dollars of transactions in SIC factor group 02 ctdsfaO3 current day total dollars of transactions in SIC factor group 03 W094/06l03 21440~8 - 12- PCI/US93/08400 ctdsfaO4 current day total dollars of trzln~c.tinns in SIC factor group 04 ctdsfaO5 current day total dollars of transactions in SIC factor group 05 ctdsfaO6 current day total dollars of transactions in SIC factor group 06 ctdsfaO7 current day total dollars of transactions in SIC factor group 07 ctdsfaO8 current day total dollars of transactions in SIC factor group 08 ctdsfaO9 current day total dollars of transactions in SIC factor group 09 ctdsfalO current day total dollars of transactions in SIC factor group lO
ctdsfal l current day total dollars of transactions in SIC factor group l l ctdsraOl current day total dollars of transactions in SIC fraud rate group Ol ctdsraO2 current day total dollars of transactions in SIC fraud rate group 02 ctdsraO3 current day total dollars of transactions in SIC fraud rate group 03 ctdsraO4 current day total dollars of transactions in SIC fraud rate group 04 ctdsraO5 current day total dollars of transactions in SIC fraud rate group 05 ctdsraO6 current day total dollars of transactions in SIC fraud rate group 06 ctdsraO7 current day total dollars of transactions in SIC fraud rate group 07 ctdsvaO l current day total dollars in SIC VISA group O l ctdsvaO2 current day total dollars in SIC VISA group 02 ctdsvaO3 current day total dollars in SIC VISA group 03 ctdsvaO4 current day total dollars in SIC VISA group 04 ctdsvaO5 current day total dollars in SIC VISA group 05 ctdsvaO6 current day total dollars in SIC VISA group 06 ctdsvaO7 current day total dollars in SIC VISA group 07 ctdsvaO8 current day total dollars in SIC VISA group 08 ctdsvaO9 current day total dollars in SIC VISA group 09 ctdsvalO current day total dollars in SIC VISA group lO
ctdsval l current day total dollars in SIC VISA group l l ctnsfaOl current day total number of transactions in SIC factor group Ol ctnsfaO2 current day total number of transactions in SIC factor group 02 ctnsfaO3 current day total number of transactions in SIC factor group 03 ctnsfaO4 current day total number of transactions in SIC factor group 04 ctnsfaO5 current day total number of transactions in SIC factor group 05 ctnsfaO6 current day total number of transactions in SIC factor group 06 ctnsfaO7 current day total number of transactions in SIC factor group 07 ctnsfaO8 current day total number of transactions in SIC factor group 08 ctnsfaO9 current day total number of transactions in SIC factor group 09 ctnsfalO current day total number of transactions in SIC factor group lO
ctnsfal l current day total number of transactions in SIC factor group l l WO 94/06103 ;~ ~ 4 4 ~ t~ 8 PCI/US93/08400 ctnsraOl current day total number of transactons in SIC fraud rate group 01 ctnsraO2 current day total number of transactons in SIC fraud rate group 02 ctnsraO3 current day total number of transactons in SIC fraud rate group 03 ctnsraO4 current day total number of transactons in SIC fraud rate group 04 ctnsraO5 current day total number of transactons in SIC fraud rate group 05 ctnsraO6 current day total number of transactons in SIC fraud rate group 06 ctnsraO7 current day total number of transactons in SIC fraud rate group 07 ctnsvaO1 current day total number in SIC VISA group Ol ctnsvaO2 current day total number of SIC VISA group 02 ctnsvaO3 current day total number of SIC VISA group 03 ctnsvaO4 current day total number of SIC VISA group 04 ctnsvaO5 current day total number of SIC VISA group 05 ctnsvaO6 current day total number of SIC VISA group 06 ctnsvaO7 current day total number of SIC VISA group 07 ctnsvaO8 current day total number of SIC VISA group 08 ctnsvaO9 current day total number of SIC VISA group 09 ctnsvalO current day total number of SIC VISA group 10 ctnsval l current day total number of SIC VISA group l l 7 Day Cardholder Fraud Related Variables raudymdy 7 day ratio of auth days over number of days in the window ravapvdl 7 day mean dollar amount for an approval ravaudl 7 day mean dollars per auth across window rddapv 7 day mean dollars per day of a~lov~ls rddapv2 7 day mean dollars per day of approvals on days with auths rddau 7 day mean dollars per day of auths on days with auths rddauall 7 day mean dollars per day of auths on all days in window rddcsapv 7 day mean dollars per day of cash approvals rddcsdec 7 day mean dolalrs per day of cash declines rdddec 7 day mean dollars per day of declines - rdddec2 7 day mean dollars per day of declines on days with auths rddmrapv 7 day mean dollars per day of merchandise approvals rddmrdec 7 day mean dollars per day of merchandise declines rdnapv 7 day mean number per day of approvals rdnau 7 day mean number per day of auths on days with auths rdnauall 7 day mean number per day of auths on all days in window WO 94tO6103 i~ - PCr/US93/08400 2 ~ 8 - 14-rdncsapv 7 day mean number per day of cash approvals r-ln~s-l~c 7 day mean number per day of cash declines rdndec 7 day mean number per day of declines rdnmrapv 7 day mean number per day of merchandise approvals rdnmrdec 7 day mean number per day of merchandise declines rdnsdap2 7 day mean number per day of approvals on same day of week calculated only for those days which had approvals rdnsdapv 7 day mean number per day of approvals on same day of week as current day rdnwdaft 7 day mean number per day of weekday afternoon approvals rdnwdapv 7 day mean number per day of weekday approvals rdnwdeve 7 day mean number per day of weekday evening approvals rdnwdmor 7 day mean number per day of weekday morning approvals rdnwdnit 7 day mean number per day of weekday night approvals rdnweaft 7 day mean number per day of weekend afternoon approvals rdnweapv 7 day mean number per day of weekend approvals rdnweeve 7 day mean number per day of weekend evening approvals rdnwemor 7 day mean number per day of weekend morning approvals rdnwenit 7 day mean number per day of weekend night approvals rhibal 7 day highest window balance rhidcapv 7 day highest dollar amt on a single cash approve rhidcdec 7 day highest dollar amt on a single cash decline rhidmapv 7 day highest dollar amt on a single merch approve rhidmdec 7 day highest dollar amt on a single merch decline rhidsapv 7 day highest dollar amount on a single approve rhidsau 7 day highest dollar amount on a single auth rhidsdec 7 day highest dollar amount on a single decline rhidtapv 7 day highest total dollar amount for an approve in a single day rhidtau 7 day highest total dollar amount for any auth in a single day rhidtdec 7 day highest total dollar amount for a decline in a single day rhinapv 7 day highest number of approves in a single day rhinau 7 day highest number of auths in a single day rhindec 7 day highest number of declines in a single day rnaudy 7 day number of days in window with any auths rnausd 7 day number of same day of week with any auths rnauwd 7 day number of weekday days in window with any auths rnauwe 7 day number of weekend days in window with any auths rncsandy 7 day number of days in window with cash auths W094/06103 ~ a~ Pcr/US93/08400 - 15~
rmnraudy 7 day number of days in window with merchant auths rtdapv 7 day total dollars of approvals rtdau 7 day total dollars of auths rtdcsapv 7 day total dollars of cash advance approvals rtdcsdec 7 day total dollars of cash advance declines rtddec 7 day total dollars of declines rtdmrapv 7 day total dollars of merchandise approvals rtdmrdec 7 day total dollars of merchandise declines rtnapv 7 day total number of approvals rtnapvdy 7 day total number of approves in a day rtnau 7 day total number of auths rtnaulOd 7 day number of auths in window <=$10 rtncsapv 7 day total number of cash advance approvals rtncsdec 7 day total number of cash advance declines rtndec 7 day total number of declines rtnmrapv 7 day total number of merch~n~li.ce approvals rtnmrdec 7 day total number of merchandise declines rtnsdapv 7 day total number of approvals on the same day of week as current dayrtnwdaft 7 day total number of weekday afternoon approvals rtnwdapv 7 day total number of weekday approvals rtnwdeve 7 day total number of weekday evening approvals rtnwdmor 7 day total number of weekday morning approvals rtnwdnit 7 day total number of weekday night approvals rtnweaft 7 day total number of weekend afternoon approvals rtnweapv 7 day total number of weekend approvals rtnweeve 7 day total number of weekend evening approvals rtnwemor 7 day total number of weekend morning approvals rtnwenit 7 day total number of weekend night approvals rvraudl 7 day variance of dollars per auth across window Profile Cardholder Fraud Related Variables paudymdy - profile ratio of auth days over number of days in the month - pavapvdl - profile mean dollar amount for an approval pavaudl - profile mean dollars per auth across month pchdzip - profile the last zip of the cardholder pdbm - profile value of 'date became member' at time of last profile update WO 94/06103 PCr/US93/08400 ~14~068 -16- ~
pddapv - profile daily mean dollars of approvals pddapv2 - profile daily mean dollars of approvals on days with auths pddau - profile daily mean dollars of auths on days with auths pddau30 - profile daily mean dollars of auths on all days in month pddcsapv - profile daily mean dollars of cash approvals pddcsdec - profile daily mean dollars of cash declines pdddec - profile daily mean dollars of declines pdddec2 - profile daily mean dollars of declines on days with auths pddmrapv - profile daily mean dollars of merchandise approvals pddmrdec - profile daily mean dollars of merchandise declines pdnapv - profile daily mean number of approvals pdnau - profile daily mean number of auths on days with auths pdnau30 - profile daily mean number of auths on all days in month pdncsapv - profile daily mean number of cash approvals pdncsdec - profile daily mean number of cash declines pdndec - profile daily mean number of declines pdnmrapv - profile daily mean number of merchandise approvals pdnmrdec - profile daily mean number of merchandise declines pdnwlap2 - profile mean number of approvals on Sundays which had auths pdnwlapv - provilde mean number of approvals on Sundays (day 1 of week) pdnw2ap2 - profile mean number of approvals on Mondays which had auths pdnw2apv - profile mean number of approvals on Mondays (day 2 of week) pdnw3ap2 - profile mean number of approvals on Tuesdays which had auths pdnw3apv - profile mean number of approvals on Tuesdays (day 3 of week) pdnw4ap2 - profile mean number of approvals on Wednesdays which had auths pdnw4apv - profile mean number of approvals on Wednesdays (day 4 of week) pdnwSap2 - profile mean number of approvals on Thursdays which had auths pdnwSapv - profile mean number of approvals on Thursdays (day 5 of week) pdnw6ap2 - profile mean number of approvals on Fridays which had auths pdnw6apv - profile mean number of approvals on Fridays (day 6 of week) pdnw7ap2 - profile mean number of approvals on Saturdays which had auths pdnw7apv - profile mean number of approvals on Saturdays (day 7 of week) pdnwdaft - profile daily mean number of weekday afternoon approvals pdnwdapv - profile daily mean number of weekday approvals pdnwdeve - profile daily mean number of weekday evening approvals pdnwdmor - profile daily mean number of weekday morning approvals pdnwdnit - profile daily mean number of weekday night approvals ~4~8 WO 94/06103 PCr/US93/08400 pdnweaft - profile daily mean number of weekend afternoon approvals pdnweapv - profile daily mean number of weekend approvals pdnweeve - profile daily mean number of weekend evening approvals pdnwemor - profile daily mean number of weekend morning approvals pdnwenit - profile daily mean number of weekend night approvals pexpir - profile expiry date stored in profile; update if curr date>pexpir phibal - profile highest monthly balance phidcapv - profile highest dollar amt on a single cash approve in a month phidcdec - profile highest dollar amt on a single cash decline in a month phidmapv - profile highest dollar amt on a single merch approve in a month phidmdec - profile highest dollar amt on a single merch decline in a month phidsapv - profile highest dollar amount on a single approve in a month phidsau - profile highest dollar amount on a single auth in a month phidsdec - profile highest dollar amount on a single decline in a month phidtapv - profile highest total dollar amount for an approve in a single day phidtau - profile highest total dollar amount for any auth in a single day phidtdec - profile highest total dollar amount for a decline in a single day phinapv - profile highest number of approves in a single day phinau - profile highest number of auths in a single day phindec - profile highest number of declines in a single day pmlavbal - profile average bal. during 1st 10 days of mo.
pmlnauths - profile number of auths in the 1st 10 days of mo.
pm2avbal - profile average bal. during 2nd 10 days of mo.
pm7n~uthc - profile number of auths in the 2nd 10 days of mo.
pm3avbal - profile average bal. during rem~ining days pm3nauths - profile number of auths in the last part of the month.
pmovewt - profile uses last zip to ~let.q.rmine recent residence move; pmovewt=2 for a move within the previous calendar month; pmovew pnaudy - profile number of days with auths pnauw 1 - profile number of Sundays in month with any auths pnauw2 - profile number of Mondays in month with any auths pnauw3 - profile number of Tuesdays in month with any auths pnauw4 - profile number of We-lnes~l~ys in month with any auths pnauwS - profile numberof Thursdays in month with any auths pnauw6 - profile number of Fridays in month with any auths pnauw7 - profile number of Saturdays in month with any auths pnauwd - profile number of weekday days in month with any auths 2 ~ 8 - 18 -pnauwe - profile number of weekend days in month with any auths pncsaudy - profile number of days in month with cash auths pnmraudy - profile number of days in month with merchant auths pnweekday - profile number of weekday days in the month pnweekend - profile number of weekend days in the month pratdcau - profile ratio of declines to auths profage - profile number of months this account has had a profile (up to 6 mo.) psdaudy - profile standard dev. of # days between transactions in a month psddau - profile standard dev. of $ per auth in a month ptdapv - profile total dollars of approvals in a month ptdau - profile total dollars of auths in a month ptdaudy - profile total dollars of auths in a day ptdcsapv - profile total dollars of cash advance approvals in a month ptdcsdec - profile total dollars of cash advance declines in a month ptddec - profile total dollars of declines in a month ptdmrapv - profile total dollars of merchandise approvals in a month ptdmrdec - profile total dollars of merchandise declines in a month ptdsfaOl - profile total dollars of transactions in SIC factor group 01 ptdsfaO2 - profile total dollars of transactons in SIC factor group 02 ptdsfaO3 - profile total dollars of transactions in SIC factor group 03 ptdsfaO4 - profile total dollars of transactions in SIC factor group 04 ptdsfaO5 - profile total dollars of transactions in SIC factor group 05 ptdsfaO6 - profile total dollars of transactions in SIC factor group 06 ptdsfaO7 - profile total dollars of transactions in SIC factor group 07 ptdsfaO8 - profile total dollars of transactions in SIC factor group 08 ptdsfaO9 - profile total dollars of transactions in SIC factor group 09 ptdsfalO - profile total dollars of transactions in SIC factor group 10 ptdsfal 1 - profile total dolalrs of transactions in SIC factor group 11 ptdsraO1 - profile total dollars of transactions in SIC fraud rate group 01 ptdsraO2 - profile total dollars of transactions in SIC fraud rate group 02 ptdsraO3 - profile total dollars of transactions in SIC fraud rate group 03 ptdsraO4 - profile total dollars of transactions in SIC fraud rate group 04 ptdsraO5 - profile total dollars of transactions in SIC fraud rate ~roup 05 ptdsraO6 - profile total dollars of transactions in SIC fraud rate group 06 ptdsraO7 - profile total dollars of transactions in SIC fraud rate group 07 ptdsvaO1 - profile total dollars in SIC VISA group 01 ptdsvaO2 - profile total dollars in SIC VISA group 02 WO 94/06103 2 1 4 ~ 0 6 ~ PCrtUS93/08400 .
ptdsvaO3 - profile total dollars in SIC VISA group 03 ptdsvaO4 - profile total dollars in SIC VISA group 04 ptdsvaO5 - profile total dollars in SIC VISA group 05 ptdsvaO6 - profile total dollars in SIC VISA group 06 ptdsvaO7 - profile total dollars in SIC VISA group 07 ptdsvaO8 - profile total dollars in SIC VISA group 08 ptdsvaO9 - profile total dollars in SIC VISA group 09 ptdsvalO - profile total dollars in SIC VISA group 10 ptdsval 1 - profile total dollars in SIC VISA group 11 ptnapv - profile total number of approvals in a month ptnapvdy - pro~lle total number of approves a day ptnau - profile total number of auths in a month ptnaulOd - profile number of auths in monthc=$10 ptnaudy - profile total number of auths in a day ptncsapv - profile total number of cash advance approvals in a month ptncsdec - profile total number of cash advance declines in a month ptndec - profile total number of declines in a month ptndecdy - profile total number of declines in a day ptnmrapv - profile total number of merchandise approvals in a month ptnmrdec - profile total number of merch~n~ e declines in a month ptnsfaO1 - profile total number of transactions in SIC factor group 01 ptnsfaOl - profile total number of transactions in SIC factor group 02 ptnsfaO3 - profile total number of transactions in SIC factor group 03 ptnsfaO4 - profile total number of transactions in SIC factor group 04 ptnsfaO5 - profile total number of transactions in SIC factor group 05 ptnsfaO6 - profile total number of transactions in SIC factor group 06 ptnsfaO7 - profile total number of transactions in SIC factor group 07 ptnsfaO8 - profile total number of transactions in SIC factor group 08 ptnsfaO9 - profile total number of transactions in SIC factor group 09 ptnsfalO - profile total number of transactions in SIC factor group 10 ptnsfal 1 - profile total number of transactions in SIC factor group 11 ptnsraOl - profile total number of transactions in SIC fraud rate group 01 ptnsraO2 - profile total number of transactions in SIC fraud rate group 02 ptnsraO3 - profile total number of transactions in SIC fraud rate group 03 ptnsraO4 - profile total number of transactions in SIC fraud rate group 04 ptnsraO5 - profile total number of transactions in SIC fraud rate group 05 ptnsraO6 - profile total number of transactions in SIC fraud rate group 06 7 ~
WO 94/06103 ~ E, ~ PCI/US93/08400 2~4~68 -20- --ptnsraO7 - profile total number of transactions in SIC fraud rate group 07 ptnsvaOl - profile total number in SIC VISA group 01 ptnsvaO2 - profile total number in SIC VISA group 02 ptnsvaO3 - profile total number in SIC VISA group 03 ptnsvaO4 - profile total number in SIC VISA group 04 ptnsvaO5 - profile total number in SIC VISA group 05 ptnsvaO6 - profile total number in SIC VISA group 06 ptnsvaO7 - profile total number in SIC VISA group 07 ptnsvaO8 - profile total number in SIC VISA group 08 ptnsvaO9 - profile total number in SIC VISA group 09 ptnsvalO - profile tot~l number in SIC VISA group 10 ptnsva 11 - profile total number in SIC VISA group 11 ptnwlapv - profile total number of a~pr~vals on Sundays (day l of week) ptnw2apv - profile total number of a~p,ovals on Mondays (day 2 of week) ptnw3apv - profile total number of approvals on Tuesdays (day 3 of week) ptnw4apv - profile total number of approvals on Wednesdays (day 4 of week) ptnw5apv - profile total number of approvals on Thursdays (day 5 of week) ptnw6apv - profile total number of a~plovals on Fridays (day 6 of week) ptnw7apv - profile total number of approvals on Saturdays (day 7 of week) ptnwdaft - profile total number of weekday afternoon approvals in a month ptnwdapv - profile total number of weekday approvals in a month ptnwdeve - profile total number of weekday evening approvals in a month ptnwdmor - profile total number of weekday morning approvals in a month ptnwdnit - profile total number of weekday night approvals in a month ptnweaft - profile total number of weekend afternoon approvals in a month ptnweapv - profile total number of weekend approvals in a month ptnweeve - profile total number of weekend evening approvals in a month ptnwemor - profile total number of weekend morning approvals in a month ptnwenit - profile total number of weekend night approvals in a month pvdaybtwn - profile variance in number of days between trx's (min of 3 trx) pvraudl - profile variance of dollars per auth across month MERCHANT FRAUD VARIABLES
mtotturn Merchant Total turnover for this specific merchant msicturn Merchant Cum~ tive SIC code turnover mctrtage Merchant Contract age for specific merchant WO 94/06103 ~ 1 4 4 ~ 6 ~ PCI`/US93/08400 m~slg.sjc Merchant Average contract age for this SIC code mavgnbtc Merchant Average number of transactions in a batch m~mttrX Merchant Average amount per transaction (average amount per authorization) mvaramt Merchant Variance of amount per transaction mavgtbtc Merchant Average time between batches mavgtaut Merchant Average time between authorizations for this merchant mratks Merchant Ratio of keyed versus swiped transactions mnidclac Merchant Number of identical customer accounts mnidcham Merchant Number of identical charge amounts mtrxsrc Merchant What is the source of transaction (ATM, merchant, etc.) ll~Ll~,~ Merchant How is the transaction transported to the source (terminal, non-terminal, voice authorization) mfloor Merchant Floor limit mchgbks Merchant Charge-backs received mrtrvs Merchant Retrievals received (per SIC, merchant, etc.). The issuer pays for a retrieval.
macqrat Merchant Acquirer risk management rate (in Europe one merchant can havemultiple acquirers, but they dont have records about how many or who.) mprevrsk Merchant Previous risk management at this merchant? Yes or No mtyprsk Merchant Type of previous risk management (counterfeit, multiple imprint, lost/stolen/not received) msicrat Merchant SIC risk management rate mpctaut Merchant Percent of transactions authorized Network Training: Once pre-procescing is complete, the fraud-related variables are fed to the network and the network is trained. The preferred embodiment uses a modeling technique known as a "feed forward" neural network. This type of network estim~t~s parameters which define relationships among variables using a training method. The preferred training method, well known to those skilled in the art, is called "backpropagation gradient descent optimi~tion~
although other well-known neural network training techniques may also be used.
One problem with neural n~Lwolh~, built with conventional backpropagation methods is - insufficient generalizability. Generalizability is a measure of the predictive value of a neural network. The attempt to m~ximi7e generalizability can be interpreted as choosing a network model with enough complexity so as not to underfit the data but not too much complexity so as to overfit the data. One measure of the complexity of a network is the number of hidden processing elements, so that the effort to maximize generalizability translates into a selection among models having different numbers of hidden processing elements. Unfortunately, it is 2~0~8 -22-often not possible to obtain all the nonlinearity required for a problem by adding hidden proce~cing elements without introducing excess complexity. Many weights that come with the addition of each new hidden proces~ing element may not be required or even helpful for the modeling task at hand. These excess weights tend to make the network fit the idiosyncrasies or "noise" of the data and thus fail to generalize well to new cases. This problem, known as overfitting, typically arises because of an excess of weights.
Weight decay is a method of developing a neural network that minimi7los overfitting without sacrificing the predictive power of the model. This method initially provides the network with all the nonlinearity it needs by providing a large number of hidden procçscing elements. Subsequently, it decays all the weights to varying degrees so that only the weights that are nPcçcs~ry for the approximation task remain. Two central premises are employed: 1 ) when given two models of equivalent performance on a training data set, favor the smaller model; and 2) implement a cost function that penalizes complexity as part of the backpropaga-tion algo~ l.. The network is trained by minimi7ing this cost function. Complexity is only justified as it expresses information contained in the data. A weight set that embodies all or almost all of the information in the data and none of the noise will maximize generalizability and perform~n~e The cost function is constructed by introducing a "decay term" to the usual error function used to train the network. It is clesign~d to optimize the model so that the network captures all the important information in the training set, but does not adapt to noise or random characteristics of the training set. In view of these requirements, the cost function must take into account not only prediction error, but also the signific~nce of model weights. A combination of these two terms yields an objective function which, when minimi7~d, generalizes optimally.
Performing a conventional gradient descent with this objective function optimizes the model.
In introducing the decay term, an assumption is made about what constitutes information.
The goal is to choose a decay term that accurately hypothesizes the prior distribution of the weights. In finding a good prior distribution, one examines the likelihood that the weights will have a given distribution without knowledge of the data.
Weigend et al, "Generalization by Weight-Flimin~tion with Application to Forecasting", in Advances in Neural Information Processing Systems 3~ pp. 875-82, and incorporated herein by reference, discloses the following cost function for weight decay:
arget~--output~,) +~ +~l~ 2/~ 2 ( where:
D is the data set;
targetk iS the target, or desired, value for element k of the data set;
WO 94/06103 ~ Pcr/US93/0840o 23 ~
outputk iS the network output for element k of the data set;
I represents the relative importance of the complexity term;
W is the weight set;
wl is the value of weight i; and wO is a constant that controls the shape of the curve that penalizes the weights.
The first term of the Weigend function measures the perforrnance of the network, while the second term measures the complexity of the network in terms of its size. With this cost function, small weights decay rapidly, while large weights decay slowly or not at all.
A major failing of the Weigend cost function, and similar weight decay schemes, is that they do not accurately mimic the intenlle~l prior distribution. Finding a good prior distribution (or 'prior") is a key element to developing an effective model. Most of the priors in the literature are sufficient to demonstrate the concept of weight decay but lack the strengths required to accommodate a wide range of problems. This occurs because the priors tend to decay weights evenly for a given procescing element, without sufficiently distinguishing important weights (which contain more information) from uni~ o, lallt weights (which contain less information). This often results either in 1) undesired decaying of important weights, which (liminiches the power of the system to accommodate nonlinearity, or 2) undesired retention of excess unimportant weights, which leads to overfitting.
The present invention uses the following improved cost function, which addresses the above problems:
--~ (targetk--outputk )2 + gl~, (ClWi2 ~ J (Eq. 2) 2 keD ieW 1 + ¦ Wj l where g represents a new term known as the interlayer gain multiplier for the decay rate, and c, is a constant. The interlayer gain multiplier takes into account the relative proximity of the weights to the input and output ends of the network. Thus, the interlayer gain multiplier al-lows application of the decay term with greater potency to elements that are closer to the inputs, where the majority of the weights typically reside, while avoiding excessive decay on weights corresponding to elements closer to the outputs, which are more critical, since their elimin~tion can effectively sever large numbers of input-side weights.
By intensifying decay on input-side elements, the cost function of Equation 2 improves the ability of model development component 801 to decay individual weights while preserving proces~ing elements cont~ining valuable information. The result is that weak interactions are elimin~tto-l while valid interactions are retained. By retaining as many processing elements as possible, the model does not lose the power to model nonlinearities, yet the ovçrfittin~ problem is reduced because unnecessary individual weights are removed.
WO 94/06103 PCI/US~3/08400 2~4~8 -24-Once the cost function has been iteratively applied to the network, weights that have been decayed to a very small number (defined as e) are removed from the network. This step, known as "thresholding the net" is pelr~lll.ed because it is often difficult to completely decay weights to zero.
Once the network has been trained using past data, the network's model definition is stored in data files. One portion of this definition, called the "CFG" file, specifies the parameters for the network's input variables, including such information as, for example, the lengths of the variables, their types, and their ranges. Referring now to Figure 21, there is shown a portion of a typical CFG file, specifying parameters for an ACCOUNT variable 2101 (representing a customer account number) and a PAUDYMDY variable 2102 (a profile variable representing the ratio of transaction days divided by the number of days in the month).
The file formats used to store the other model definition files for the network are shown below.
ASCII File Formats The ASCII network data files (.cta, .sta, .Ica, .wta) consist of tokens (non-whitespace) separated by whitespace (space, tab, newline).
Whitespace is ignored except to separate tokens. Use of line breaks and tabs is encouraged for clarity, but otherwise irrelevant.
File format notation is as follows:
* Br~ck~te~l text denotes a token.
* Nonbracketed text denotes a literal token which must be matched exactly, including case.
* Comments on the right are not part of the file format; they simply provide further description of the format.
* In the comments, vertical lines denote a block which can be repeated. Nested veltical lines denote repeatable sub-blocks.
21~6~
.cta Format Fileformat Comments cts <NetName>
<Value> I Repeated as needed cts and <NetName> must appear first. <NetName>is the standard abbreviation, lowercase (e.g., mbpn). The <Value>s are the network constants values, in the order defined within the constants structure. If a constants value is an array or structured type, each element or field must be a separate token, appearing in the proper order.
Example Comrnents cts mbpn 2 InputSize OutputSize cHidSlabs 2 HiddenSize[0]
o HiddenSize[ 1 ]
0 HiddenSize[2]
3 p~ntlom.Seed 1 . 0 InitWeightMax 0 WtsUpdateFlag 0 ConnectInputs 0 FnClass 1. 0 Parml 1 . 0 Parm2 -1 . 0 Parm3 0 . 0 Parm4 0 . 0 Parm5 cEntTbl xLow 0 . 1 xHigh O . 2 HiddenAlpha[0]
O . O HiddenAlpha[1]
Wo 94/06103 ~ PCI/US93/08400 21~68 -26- ~
Example Comments 0 . 0 HiddenAlpha[2]
0 . 1 OutputAlpha 0 . 9 HiddenBeta[0]
0 . 0 HiddenBeta[l]
0 . 0 HiddenBeta[2]
0 . 9 OutputBeta 0 . 0 Tolerance 0 WtsUpdateFlag 0 BatchSize 0 LinearOutput 0 ActTblFlag StatsFlag LearnFlag In this example, HiddenSize, HiddenAlpha, and HiddenBeta are all arrays, so each element (0, l, 2) has a separate token, in the order they appear in the type.
.sta Format File format Cornments sts <NetName>
<cSlab>
<nSlab> I Repeated cSlab times <cPe>
<state> I I RepeatedcPetimes sts and <NetName> must appear first. <NetName>is the standard abbreviation, lower-case. <cSlab>is a count of the slabs which have states stored in the file. The remainder of the file consists of cSlab blocks, each describing the states of one slab. The order of the slab blocks in the file is not hlll)olL~lt.<nslab>is the slab number, as defined in the ~ .h file. cPeis the number of states for the slab. <state>is the value of a single state. If the state type is an array or structured type, each element or field must be a separate token, appearing in the proper order. There should be cPe <state> values in the slab bloclc.
WO 94/06103 214 4 ~ G 8 PCr/US93/08400 13xample Comments sts mbpn 6 cSlab O nSlab -- SlabInMbpn 2 cPeIn O . O StsIn[O]
O . O StsIn[1]
nSlab -- SlabTrnMbpn cPeTrn O . O StsTrn[O]
2 nSlab -- SlabHidOMbpn 2 cPeHidO
O . O StsHidO[O]
O . O StsHidO[l]
nSlab -- SlabOutMbpn cPeOut O . O StsOut[O]
6 nSlab -- SlabBiasMbpn cPeBias 1 . o StsBias[O]
7 nSlab -- SlabStatMbpn 3 cPeStat O . O StsStat[O]
O . O StsStat[1]
O O StsStat[2 i .Ica Format File format Comments lcl <NetName>
<cSlab>
<nSlab> I Repeated cSlab times <cPe>
<local> i I Repeated cPe times WO 94/06103 ' ~ PCI/US93/08400 ~1~4~68 -28-The .Ica format is just like the .sta format except that sts is replaced by lcl. lcl and <NetName> must appear first. <NetName>is the standard abbreviation, lowercase.
<cSlab>is a count of the slabs which have local data stored in the file. The rem~inder of the file consists of cSlab blocks, each describing the local data values of one slab.
<nSlab>is the slab number, as defined in the ~x.h file. The order of the slab blocks in the file is not important. cPeis the number of local data values for the slab. <local> is the value of a single local data element. If the local data type is an array or structured type, each element or field must be a separate token, appearing in the proper order. There should be cPe <local> values in the slab block.
Fxample Comments lcl mbpn 3 cSlab 2 nSlab -- SlabHidOMbpn 2 cPe 0.0 LclHidO[O].Error 0.0 LclHidO[O].NetInp 0.0 LclHidO[ 1 ] .Error 0.0 LclHidO[l].NetInp nSlab -- SlabOutMbpn cPe 0.0 LclOut[O].Error 0.0 LclOut[O].NetInp 7 nSlab -- SlabStatMbpn 3 cPe O LclStat[O].cIter 0.0 LclStat[O].Sum O LclStat[l].cIter 0.0 LclStat[l].Sum O LclStat[2].cIter 0.0 LclStat[2].Sum In this example, the <local> values are all structured types, so each field (Error and NetInp; cIter and Sum) has a separate token, in the order they appear in the type.
WO 94/06103 2 1 ~ 8 Pcr/US93/08400 .wta Format File formatComments wts <NetName>
<cClass>
<nSlab> I Repeated cClass times <nClass>
<cIcn>
<weight> I I RepeatedcIcntimes wts and <NetName> must appear first. <NetName>is the standard abbreviation, lowercase. <cClass>is a count of the slab/class combinations which have weights stored in the file. The remainder of the file consists of cClass blocks, each describing the weights of one slab. The order of the class blocks in the file is not important. <nSlab>is the slab number, as defined in the ~x.h file. <nClass>is the class number, as defined in the ~x.h file. ~weight>is the value of a single weight. If the weight type is an array or structured type, each element or field must be a separate token, appearing in the proper order. There should be cIcn <weight> values in the slab block.
Example Comments wts mbpn 2 cClass 2 nSlab -- SlabHidOMbpn O nClass -- PeHidOMbpnFromPrev cIcn O . O WtsHidO[PE_O][O]
0 . O WtsHidO[PE_O][l]
O . O WtsHidO[PE_0]~2~
O . O WtsHidO[PE_l][O]
O . 0 WtsHidO[PE_l][l]
O . 0 WtsHidO[PE_1][2]
nSlab -- SlabOutMbpn O nClass -- PeOutMbpnFromPrev 3 cIcn O . O WtsOut[PE_O][O]
WO 94/06103 ~ ~ ~ PCI/US93/08400 21~4~68 ~30-Fxample Comments 0 . 0 WtsOut[PE_0][1]
O . 0 WtsOut[PE_0][2]
Weights values for a slab and class are stored as a one-~lim~n~ional array, but conceptually are indexed by two values -- PE and interconnect within PE. The values are stored in row-major order, as exemplified here.
Transaction Processing Component 802 Once the model has been created, trained, and stored, fraud detection may begin.Transaction processing component 802 of system 100 preferably runs within the context of a conventional authorization or posting system for customer transactions. Transaction processing component 802 reads current transaction data and customer data from databases 805, 806, and generates as output fraud scores representing the likelihood of fraud for each transaction.
Furthermore, transaction proces~ing component 802 can compare the likelihood of fraud with a predetermined threshold value, and flag transactions for which the threshold is exceeded.
The current transaction data from ~t~h~e 805 typically includes information such as:
transaction dollar amount; date; time (and time zone if necessary); approve/decline code;
cash/merch~n~ e code; available credit (or balance); credit line; merchant category code;
merchant ZIP code; and P~ v~.rifi~tion (if applicable).
The customer data from database 806 typically includes information from three sources:
1) general information on the customer; 2) data on all approved or declined transactions in the previous seven days; and 3) a profile record which contains data describing the customer's transactional pattern over the last six months. The general information on the customer typically includes information such as: customer ZIP code; account open date; and expiration date. The profile record is a single record in a profile database summarizing the customer' s transactional pattern in terms of moving averages. The profile record is updated periodically (usually monthly) with all of the transactions from the period for the customer, as described below.
System 100 can operate as either a batch, semi-real-time, or real-time system. The structure and proce~ing flow of each of these variations will now be described.
Batch System: Figure 14 shows operation of a batch system. Transactions are recorded throughout the day or other convenient period 1402. At the end of the day, the system performs steps 1403 to 1409 for each transaction. It obtains data describing the current transaction 1403, as well as past transaction data, customer data, and profile data 1404. It then applies this data to the neural network 1405 and obtains a fraud score 1406. If the fraud score exceeds a threshold 1407, the account is flagged 1408. In the batch system, therefore, the transaction which yielded the high fraud score cannot itself be blocked; rather, the account is flagged 1404 at the end of the day so that no future transactions are possible. Although the batch system does not permit WO 94/06103 2~sl4 ~ 6 8 PCr/US93/08400 immediate detection of fr~n~l~-lçnt transactions, response-time constraints may m~n~l~te use of the batch system in some implementations.
Semi-Real-Time System: The semi-real-time system operates in a similar manner to the batch system and uses the same data files, but it ensures that no more than one high-scoring transaction is authorized before flagging the account. In this system, as shown in Figure 15, fraud likelihood determin~tion is performed (steps 1504 to 1509) immediately after the transaction is authorized 1503. Steps 1504 to 1509 correspond to steps 1403 to 1409 of the batch system illustrated in Figure 14. If the likelihood of fraud is high, the account is flagged 1509 so that no future transactions are possible. Thus, as in the batch system, the current trans-action cannot be blocked; however, the serni-real-time system allows subsequent transactions to be blocked.
Real-Time System: The real-time system performs fraud likelihood determination before a transaction is authorized. Because of response-time constraints, it is preferable to minimi7e the number of ~l~t~b:~e access calls when using the real-time system. Thus, in this embodiment, all of the customer information, including general information and past transaction data, is found in a single record of profile database 806. Profile ~l~t~ba~e 806 is generated from past transaction and customer data before the transaction proces~ing component starts operating, and is updated after each transaction, as described below. Because all needed data are located in one place, the system is able to retrieve the data more quickly than in the batch or semi-real-time schemes. In order to keep the profile database 806 current, profile records are updated, using moving averages where applicable, after each transaction.
Referring now to Figure 16, there is shown a flowchart of a real-time system using the profile database. Upon receiving a merchant's request for authorization on a transaction 1602, the system obtains data for the current transaction 1603, as well as profile data snmm~ri7ing transactional patterns for the customer 1604. It then applies this data to the stored neural network model 1605. A fraud score (representing the likelihood of fraud for the transaction) is obtained 1606 and compared to a threshold value 1607. Steps 1601 through 1607 occur before a transaction is authorized, so that the fraud score can be sent to an authorization system 1608 and the transaction blocked by the authorization system if the threshold has been exceeded. If the threshold is not exceeded, the low fraud score is sent to the authorization system 1609. The system then updates customer profile database 806 with the new transaction data 1610. Thus, in this system, profile database 806 is always up to date (unlike the batch and semi-real-time sysiems, in which profile database 806 is updated only periodically).
Referring now to Figure 12, there is shown the method of creating a profile record. The system performs the steps of this method when there is no çxi~ting profile record for the customer. The system reads the past transaction (l~t~h~ce 1101 for the past six months and the customer database 1103 (steps 1202 and 1203 respectively). It generates a new profile record WO 94/06103 ~ PCr/US93/08400 1204 with the obtained data and saves it in the profile database 1205. If there are more accounts to be processed 1206, it repeats steps 1202 through 1205.
Referring now to Figure 13, there is shown the method of updating an existing profile record. The system reads the past transaction database 1101 for the past six months, customer database 1103 and profile database (steps 1302, 1303, and 1304 respectively). It combines the data into a single value for each variable in the profile database. This value is generated using one of two formulas.
For variables that represent average values over a period of time (for example, mean dollars of transactions in a month), Equation 3 is used:
newProfData = ((1 - a) * oldProfData) + (a * currentVal)) (Eq. 3) For variables that represent extreme values over a period of time (for example, highest monthly balance), Equation 4 is used:
newProfData = max(currentVal, b * oldProfData) (Eq. 4) In Equations 3 and 4:
newProfData is the new value for the profile variable;
oldProfData is the old value for the profile variable;
currentVal is the most recent value of the variable, from the past transaction database;
and a and b are decay factors which are used to give more importance to recent months and less importance to months further in the past.
The value of b is set so that older data will "decay" at an acceptable rate. A typical value for b is 0.95.
The value of a is generated as follows: For the batch and semi-real-time systems, a is set to a value such that the contribution of the value from more than six months previous is nearly zero. For profiles that have been in existence for at least six months, the value of a is 1/6.
For newer prof1les, the value is 1/(n+1), where n is the number of months since the profile was created. For the real-time system, profile updates do not occur at regular intervals. Therefore, a is deterrnined using the following equation:
a = 1 - exp (-t/T) (Eq. 5) where:
t is the time between the current transaction and the last transaction; and -Wo 94/06103 2 ~ 6 8 PCI`/US93/08400 - 33 - , -T is a time constant for the specific variable.
Furthermore, for the real-time system, currentVal represents the value of the variable estimated solely using information related to the current transaction and the time since the last transaction, without reference to any other historical information.
Once the new values for the profile variables have been generated, they are placed in an updated prof1le record 1305 and saved in the profile d~t~h~ce. 1306. If there are more accounts to be processed 1307, the system repeats steps 1302 through 1306.
In all of these embodiments, the current transaction data and the customer data are preferably pre-processed to derive fraud-related variables which have been empirically determined to be effective predictors of fraud. This is done using the same technique and the same fraud-related variables as described above in connection with neural network training.
Referring now to Figures 17 through 19, there are shown flowcharts illustrating the operation of the preferred embodiment of the transaction processing component. Some of the individual elements of the flowchart are indicated by de.sign,.tions which correspond to module names.
Referring now to Figure 17, there is shown the overall operation of transaction processing component 802. First the system runs module CINITNET 1702, which initi,.li7es network structures. Then, it runs module CSCORE 1703. Module CSCORE 1703 uses current trans-action data, data describing transactions over the past seven days, a profile record, and customer data to generate a fraud score indicating the likelihood that the current transaction is fr~lldl-lent, as well as reason codes (described below). The system then checks to see whether there are more transactions to be processed 1704, and repeats module CSCORE 1703 for any additional transactions. When there are no more to be processed, the system runs module FREENET 1705, which frees the network structures to allow them to be used for further procescing.
Referring now to Figure 18, there is shown the operation of module CSCORE 1703.
First, module CSCORE 1703 obtains current transaction data, data describing transactions of the past seven days, the profile record, and customer data (steps 1802 through 1805). From these data, module CSCORE 1703 generates the fraud-related variables 1806 described above. Then, it runs module DeployNet 1807, which applies the fraud-related variables to the stored neural network and provides a fraud score and reason codes. CSCORE then outputs the score and reason codes 1808.
Referring now to Figure 19, there is shown the operation of module DeployNet 1807.
Module DeployNet 1807 first scales the fraud-related variables 1902 to match the scaling previously performed in model development. If the value of a variable is mic.cing, DeployNet sets the value to equal the mean value found in the training set. Then it applies the scaled variables to the input layer of neural network 108, in step 1903. In step 1904, it processes the 0 6 ~ - 34 -applied data through the network to generate the fraud score. The method of iterating the network is well known in the art.
In addition to providing fraud scores, in step 1904, module DeployNet 1807 optionally generates "reason codes". These codes intlic~t~ which inputs to the model are most important in determining the fraud score for a given transaction. Any technique that can track such reasons may be used. In the preferred embodiment, the technique set forth in co-pending U.S. ap-plication Serial No. 07/814,179, (attorney's docket number 726) for "Neural Network Having Expert System Functionality", by Curt A. Levey, filed December 30, 1991, the disclosure of which is hereby incorporated by reference, is used.
The following module descriptions snmm~rize the functions performed by the individual modules.
FALCON C FILES
FILE NAME: CINITNET
DESCRIPTION: Contains code to allocate and illiti~lli7~ the network structures.
FUNCTION NAME: CINITNET() DESCRIPTION: Allocate and initi~li7e the network structures.
FILE NAME: CSCORE
DESCRIPTION: Generates fraud related variables and iterates the neural network.
FUNCTION NAME: SCORE() DESCRIPTION: Creates fraud related variables from raw variables and makes calls to initi~li7e the input layer and iterate the neural network.
FUNCTION NAME: setInput() DESCRIPTION: Sets the input value for a procec~ing element in the input layer.
FUNCTION NAME: hiReason() DESCRIPTION: Finds the three highest reasons for the score.
FILE NAME: CFREENET
DESCRIPTION: Makes function calls to free the network structures.
~14~68 WO 94/06103 ~ PCr/US93/08400 FUNCTION NAME: CFREENET() DESCRIPTION: Frees the network structures.
F~I,E NAME: CCREATEP
DESCRIPTION: Contains the cardholder profile creation code.
FUNCTION NAME: createpf() DESCRIPTION: Creates a profile record for a cardholder using the previous month's authorizations and cardholder data.
FILE NAME: CUPDATEP
DESCRIPTION: Updates a profile of individual cardholder activity.
FIJNCTION NAME: updatepf() DESCRIPTION: Updates a profile record for a cardholder using the previous profile record values as well as the previous month's authorizations and cardholder data.
FILE NAME: CCOMMON
DESCRIPTION: This file contains functions which are needed by at least two of the following:
createpf(), updatepf(), score().
FUNCTION NAME: accumMiscCnts() DESCRIPTION: Increments counters of various types for each authorization found.
FIJNCTION NAME: ~cum~icCnts() DESCRIPTION: Increments SIC variable counters.
FUNCTION NAME: initSicCounts() DESCRIPTION: Initializes the SIC variable counters.
FIJNCTION NAME: updateSicMovAvgs() DESCRIPTION: Updates the SIC profile variables.
FUNCTION NAME: writeMiscToProfile() DESCRIPI`ION: Writes various variables to the profile record after they have been calculated.
7 .' '~
2~44~ 36-FUNCTION NAME: hncDate() DESCRIPTION: Converts a Julian date to a date indicating the number of days since Jan. 1, 1990.
FUNCTION NAME: missStr() DESCRIPTION: Checks for "mi.ccin~" flag (a period) in a null terminated string.
String must have only blanks and a period to qualify as miccing A
string with only blanks will also qualify as "mic.cing'~
Cascaded Operation One way to improve system performance is via "c~cc~(le.d" operation. In cascadedoperation, more than one neural network model is used. The second neural network model is trained by model development component 801 in a similar manner to that described earlier.
However, in training the second model, model development component 801 uses only those transactions that have fraud scores, as det~rmin~ by prior application to the first neural network model, above a predetermined c~sc~cle threshold. Thus, the second model provides more accurate scores for high-scoring transactions. While the same fraud-related variables are available to train both models, it is often the case that different variables are found to be significant in the two models.
Referring now to Figure 20, there is shown a flowchart of the operation of the transaction procescing component in a cacc~clPcl system. First, transaction processing component 802 scores each transaction using the first model 2002, as described above. Those transactions that score above the c~sc~cle threshold 2003 are applied to the second neural network model 2005. The system outputs scores and reason codes from either the first model 2004 or the second model 2006, as a~lol ,iate.
The above-described c~cc~ling technique may be extended to include three or moreneural network models, each having a corresponding cascade threshold.
Performance Monitor The system periodically monitors its performance by measuring a performance metric comprising the fraud detection rate and the false positive rate. Other factors and statistics may also be incorporated into the performance metric. When the pelro"llance metric falls below a predetermined performance level, the system may either inform the user that the fraud model needs to be redeveloped, or it may proceed with model redevelopment autom~ti~lly.
From the above description, it will be apparent that the invention disclosed herein provides a novel and advantageous method of detecting fraudulent use of customer accounts and account numbers, which achieves high detection rates while keeping false positive rates relatively low. The foregoing discussion discloses and describes merely exemplary methods and O 94/06103 Zl 44 0 6~ Pcr/US93/08400 embodiments of the present invention. As will be understood by those f~mili~r with the art, the invention may be embodied in many other specific forms without departing from the spirit or es-sential characteristics thereof. For example, other predictive modeling techniques besides neural networks might be used. In addition, other variables might be used in both the model development and transaction procçs~ing components.
Accordingly, the disclosure of the present invention is inte~(lecl to be illustrative of the preferred embodiments and is not meant to limit the scope of the invention. The scope of the invention is to be limited only by the following claims.
however, the techniques and principles discussed herein apply to other types of customer accounts, such as charge cards, bank automated teller machine cards and telephone calling cards.
Credit card issuers conventionally attempt to limit fraud losses by immediately closing a customer's account upon receiving a report that the card has been lost or stolen. Typically, the customer's credit information is then transferred to a new account and a new card is issued. This procedure is only effective in limiting fraudulent use of lost or stolen cards after the loss or theft has been reported to the issuer.
In many cases, however, fraudulent use occurs without the knowledge of the cardholder, and therefore no report is made to the issuer. This may occur if the customer is unaware that the 3 PCI~US93/08400 ~14~Q68 -2-card has been lost or stolen, or if other techniques are employed to perpetrate the fraud, such as:
use of counterfeit cards; merchant fraud; application fraud; or interception of credit cards in the mail. In all these situations, the fraudulent use may not be detected until (and unless) the card-holder notices an unf~mili~r transaction on his or her next monthly statement and contests the corresponding charge. The concomitant delay in detection of fraud may result in .~ignific~nt losses. User fraud, in which the user claims that a valid transaction is invalid, is also possible.
Issuers of credit cards have sought to limit fraud losses by attempting to detect fraudulent use before the cardholder has reported a lost or stolen card. One conventional technique is known as parameter analysis. A parameter analysis fraud detection scheme makes a decision using a small number of database fields combined in a simple Boolean condition. An example of such a condition is:
if (number of transactions in 24 hours > X) and (more than Y dollars authorized)then flag this account as high risk Parameter analysis will provide the values of X and Y that satisfy either the required detection rate or the required false positive rate. In a hypothetical example, parameter values of X=400 and Y=l000 might capture 20% of the frauds with a false positive rate of 200:1, while X=6 and Y=2000 might capture 8% of the frauds with a false positive rate of 20: l .
The rules that parameter analysis provides are easily implemented in a database management system, as they are restricted to Boolean (e.g., and, or) combinations of conditions on single variables.
Parameter analysis derives rules by e~mining the single variables most able to distinguish fraudulent from non-fraudulent behavior. Since only single-variable threshold comparisons are used, complex interactions among variables are not captured. This is a limitation that could cause the system to discrimin~te poorly between fr~1ld1l1ent and valid account behavior, resulting in low capture rates and high false-positive rates.
Additionally, an effective fraud detection model generally requires more variables than conventional parameter analysis systems can handle. Furthermore, in order to capture new fraud schemes, parameter analysis systems must be redeveloped often, and automated redevelopment is difficult to implement.
It is desirable, therefore, to have an automated system that uses available information regarding cardholders, merchants, and transactions to screen transactions and isolate those which are likely to be fraudulent, and which captures a relatively high proportion of frauds while m~int~ining a relatively low false-positive rate. Preferably, such a system should be able to handle a large number of interdependent variables, and should have capability for redevelopment of the underlying system model as new patterns of fraudulent behavior emerge.
-SUMMARY OF THE INVENTION
In accordance with the present invention, there is provided an automated system and method for detecting fraudulent transactions, which uses a predictive model such as a neural network to evaluate individual customer accounts and identify potentially fraudulent transactions based on learned relationships among known variables. These relationships enable the system to estimate a probability of fraud for each transaction. This probability may then be provided as output to a human decision-maker involved in processing the transaction, or the issuer may be signaled when the probability exceeds a predetermined amount. The system may also output reason codes that reveal the relative contributions of various factors to a particular result. Fi-nally, the system periodically monitors its performance, and redevelops the model when performance drops below a predetermined level.
BRIEF DESCRIPTION OF THE DRAWINGS
Figure 1 is a block diagram of an implementation of the present invention.
Figure 2 is a sample system monitor screen which forms part of a typical output interface for the present invention.
Figure 3 is a sample account selection screen which forms part of a typical output interface for the present invention.
Figure 4 is a sample transaction analysis screen which forms part of a typical output interface for the present invention.
Figure 5 is a sample customer information screen which forms part of a typical output interface for the present invention.
Figure 6 is a sample analyst response screen which forms part of a typical output interface for the present invention.
Figure 7 is a flowchart illustrating the major functions and operation of the present invention.
Figure 8 is a block diagram showing the overall functional architecture of the present invention.
Figure 9 is a diagram of a single proces.~ing element within a neural network.
Figure 10 is a diagram illustrating hidden processing elements in a neural network.
Figure 11 is a flowchart of the pre-processing method of the present invention.
Figure 12 is a flowchart of the method of creating a profile record of the present invention.
Figure 13 is a flowchart of the method of updating a profile record of the present invention.
WO 94/06103 i- PCI/US93/08400 2144Q68 ~4~
Figure 14 is a flowchart showing operation of a batch transaction procescing system according to the present invention.
Figure 15 is a flowchart showing operation of a semi-real-time transaction processing system according to the present invention.
Figure 16 is a flowchart showing operation of a real-time processing system according to the present invention.
Figure 17 is a flowchart showing the overall operation of the transaction proces.cing component of the present invention.
Figure 18 is a flowchart showing the operation of module CSCORE of the present invention.
Figure 19 is a flowchart showing the operation of DeployNet of the present invention.
Figure 20 is a flowchart showing c~cc~d~d operation of the present invention.
Figure 21 is a portion of a typical CFG model definition file.
DESCRIPTION OF THE PREFERRED EMBODIMENT
The Figures depict preferred embodiments of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.
Referring now to Figure 1, there is shown a block diagram of a typical implementation of a system 100 in accordance with the present invention. Transaction information is applied to system 100 via data network 105, which is connected to a conventional financial data facility 106 collecting transaction information from conventional sources such as human-operated credit-card authorization termin~l.c and automated teller machines (not shown). CPU 101 runs software program instructions, stored in program storage 107, which direct CPU 101 to perform the various functions of the system. In the preferred embodiment, the software program is written in the ANSI C language, which may be run on a variety of conventional haldwale platforms. In accordance with the software program instructions, CPU 101 stores the data ob-tained from data network 105 in data storage 103, and uses RAM 102 in a conventional manner as a workspace. CPU 101, data storage 103, and program storage 107 operate together to provide a neural network model 108 for predicting fraud. After neural network 108 processes the information, as described below, to obtain an indication of the likelihood of fraud, a signal indicative of that likelihood is sent from CPU 101 to output device 104.
In the preferred embodiment, CPU 101 is a Model 3090 IBM mainframe computer, RAM 102 and data storage 103 are conventional RAM, ROM and disk storage devices for the WO 94/06103 2 ~ 4 4 ~ 6 8 PCI/US93/08400 Model 3090 CPU, and output device 104 is a conventional means for either printing results based on the signals generated by neural network 108, or displaying the results on a video screen using a window-based interface system, or sending the results to a database for later access, or sending a signal dependent on the results to an authorization system (not shown) for further pro-cec~ing.
Referring now also to Figures 2 through 6, there are shown sample screens from aconventional window-based interface system (not shown) which forms part of output device 104.
Figure 2 shows system monitor 201 that allows a fraud analyst or system supervisor to review system performance. System monitor 201 shows a cutoff score 202 above which accounts will be flagged, the number of accounts with scores above the cutoff 203, and the fraud score 204 and account number 205 for a particular account.
Figure 3 shows account selection screen 301 that includes a scrolling window 302allowing the analyst to select high-risk transactions for review, and a set of buttons 303 allowing the analyst to select further operations in connection with the selected transactions.
Figure 4 shows transaction analysis screen 401 that allows the fraud analyst to examine each high-risk transaction and determine applop.iate fraud control actions. It includes account information 402, fraud score 403, explanations derived from reason codes 404 that indicate the reasons for fraud score 403, and two scrolling windows 405 and 406 that show transaction in-formation for the current day and the past seven days 405, and for the past six months 406.
Figure 5 shows customer information screen 501 that allows the analyst to accesscustomer information, including account number 502, customer names 503, best time to call 504, phone numbers 505, and address 506. It also provides access to further functions via on-screen buttons 507.
Figure 6 shows analyst response screen 601 that allows the analyst to log actions taken to control fraud. It includes a series of check boxes 602 for logging information, a comment box 603, and on-screen buttons 604 allowing access to other functions.
Referring now also to Figure 7, there is shown an overall flowchart illustrating the major functions and operation of the system 100. First neural network model 108 is trained 701 using data describing past transactions from data network 105. Then data describing the network model are stored 702. Once the model description is stored, system 100 is able to process current transactions. System 100 obtains data for a current transaction 703, and applies the - current transaction data to the stored network model 704. The model 108 determines a fraud score and reason codes (described below), which are output 705 to the user, or to a database, or to another system via output device 104.
Referring now to Figure 8, the overall functional architecture of system 100 is shown.
System 100 is broken down into two major components: model development component 801 and transaction processing component 802. Model development component 801 uses past data WO 94/06103 PCr/US93/08400 21~4068 -6- --804 to build neural network 108 cont~ining information representing learned relationships among a number of variables. Together, the learned relationships form a model of the behavior of the variables. Although a neural network is used in the preferred embodiment, any type of predictive modeling technique may be used. For purposes of illustration, the invention is described here in terms of a neural network.
Transaction proces.sing component 802 performs three functions: 1) it determines the likelihood of fraud for each transaction by feeding data from various sources 805, 806 into neural network 108, obtaining results, and outputting them 807; 2) when applicable, it creates a record in a profile database 806 summarizing past transactional patterns of the customer; and 3) when applicable, it updates the applol),iate record in profile database 806.
Each of the two components of the system will be described in turn.
Model Development Component 801 Neural Networks: Neural networks employ a technique of "learning" relationships through repeated exposure to data and adjustment of intern~l weights. They allow rapid model development and automated data analysis. Essentially, such networks represent a statistical modeling technique that is capable of building models from data cont~ining both linear and non-linear relationships. While sirnilar in concept to regression analysis, neural networks are able to capture nonline~rity and interactions among independent variables without pre-specification. In other words, while traditional regression analysis requires that nonline~rities and interactions be detected and specified m~nll~lly, neural networks perform these tasks autom~tic~lly. For a more detailed description of neural networks, see D.E. Rumelhart et al, "Learning Representations by Back-Propagating Errors", Nature v. 323, pp. 533-36 (1986), and R. Hecht-Nielsen, "Theory of the Backpropagation Neural Network", in Neural Networks for Perception. pp. 65-93 (1992), the teachings of which are incorporated herein by reference.
Neural networks comprise a number of interconnected neuron-like processing elements that send data to each other along connections. The strengths of the connections among the pro-cessing elements are represented by weights. Referring now to Figure 9, there is shown a diagram of a single processing element 901. The processing element receives inputs Xl, X2, ...
Xn, either from other proces~ing elements or directly from inputs to the system. It multiplies each of its inputs by a corresponding weight wl, w2, ... Wn and adds the results together to form a weighted sum 902. It then applies a transfer function 903 (which is typically non-linear) to the weighted sum, to obtain a value Z known as the state of the element. The state Z is then either passed on to another element along a weighted connection, or provided as an output signal.
Collectively, states are used to represent information in the short term, while weights represent long-term information or learning.
Processing elements in a neural network can be grouped into three categories: input Wo 94/06103 2 ~ 4 4 0 ~ PCr/USs3/08400 processing elements (those which receive input data values); output procescing elements (those which produce output values); and hidden processing elements (all others). The purpose of hidden processing elements is to allow the neural network to build intermediate representations that combine input data in ways that help the model learn the desired mapping with greater accuracy. Referring now to Figure 10, there is shown a diagram illustrating the concept of hidden processing elements. Inputs 1001 are supplied to a layer of input procescin~ elements 1002. The outputs of the input elements are passed to a layer of hidden elements 1003.
Typically there are several such layers of hidden elements. Eventually, hidden elements pass outputs to a layer of output elements 1004, and the output elements produce output values 1005.
Neural networks learn from examples by modifying their weights. The "training"
process, the general techniques of which are well known in the art, involves the following steps:
1) Repeatedly presenting examples of a particular inputloutput task to the neural network model;
2) Comparing the model output and desired output to measure error; and 3) Modifying model weights to reduce the error.
This set of steps is repeated until further iteration fails to decrease the error. Then, the network is said to be "trained." Once training is completed, the network can predict outcomes for new data inputs.
Fraud-Related Variables In the present invention, data used to train the model are drawn from various database files containing historical data on individual transactions, merchants, and customers. These data are preferably pre-processed before being fed into the neural network, resulting in the creation of a set of fraud-related variables that have been empirically deterrnined to form more effective predictors of fraud than the original historical data.
Referring now to Figure 11, there is shown a flowchart of the pre-processing method of the present invention. Individual elements of the flowchart are indicated by designations which correspond to module names.
Data used for pre-processing is taken from three databases which contain past data: 1) past transaction database 1101 (also called an "authorization database") cont~ining two years' worth of past transaction data, which may be implementecl in the same data base as past data 804; 2) customer database 1103 cont~ining customer data; and 3) fraud database 1102 which indicates which accounts had fraudulent activity and when the fraudulent activity occurred.
Module re~ th c~c 1104 reads transaction data from past transaction database 1101.
Module match~llth c:lc 1105 samples this transaction data to obtain a new transaction data set containing all of the fraud accounts and a randomly-selected subset of the non-fraud accounts.
In creating the new transaction data set, module match~nth s~c 1105 uses information from fraud database 1102 to determine which accounts have fraud and which do not. For effective network training, it has been found preferable to obtain approximately ten non-fraud accounts for every fraud account.
Module readex.sas 1106 reads customer data from customer database 1103. Module matchex.sas 1107 samples this customer data to obtain a new customer data set cont~ining all of the fraud accounts and the same subset of non-fraud accounts as was obtained by module match~--th ~c In creating the new customer data set, module matchex.sas 1107 uses informa-tion from fraud database 1102 to determine which accounts have fraud and which do not.
Module mxmerge.sas 1108 merges all of the data sets obtained by modules match~llth ~ 1105 and matchex.sas 1107. Module genau.sas 1109 subdivides the merged data set into subsets of monthly data.
Module gensamp.sas 1112 samples the data set created by module mxmerge.sas 1108 and subdivided by genau.sas 1109, and creates a new data set called sample.ssd where each record represents a particular account on a particular day with transaction activity. Module gensamp.sas 1112 determines which records are fraudulent using information from fraud database 1102. Module gensamp.sas 1112 provides a subset of authorization days, as follows:
From the database of all transactions, a set of active account-days is created by removing multiple transactions for the same customer on the same day. In the set of active account-days, each account day is assigned a "draft number" from 0 to 1. This draft number is assigned as follows: If the account-day is non-fraudulent, then the draft number is set to a random number between 0 and 1. If the account-day is fraudulent and it lies on the first or second day of fraud, then the draft number is set to 0. Otherwise, it is set to 1. Then, the 25,000 account-days with the smallest draft numbers are selected for inclusion in sample.ssd. Thus, all fraudulent account-days (up to 25,000) plus a sample of non-fraudulent account-days are included in sample.ssd.
Module rolll5.sas 1113 generates a lS-day rolling window of data. This data has multiple records for each account-day listed in sample.ssd. The current day and 14 preceding days are listed for each sample account.
Module rolll5to7.sas 1117 takes the rolll5 data set and filters out days eight to 15 to produce roll7, a 7-day rolling window data set 1119. Days eight to 15 are ignored. Module genrolv.sas 1118 generates input variables for a rolling window of the previous 15 days of transactions. It processes a data set with multiple and variable numbers of records per account and produces a data set with one record per account. The result is called rollv.ssd.
Module rolll5tol.sas 1114 takes the rolllS data set and filters out days except the current day to produce rolll . Module gencurv.sas 1115 uses roll 1 to generate current day variables 1116 describing transactions occurring during the current day.
Module genprof.sas generates profile variables which form the profile records 1111.
2~44~68 Wo 94/06103 PCI/US93/08400 g Module merge.sas 1120 combines the profile records 1111, 1-day variables 1116, and 7-day variables 1119 and generates new fraud-related variables, as listed below, from the combina-tion. It also merges rollv.ssd with the sample-filtered profile data sets to produce a single data set with both profile and rolling window variables. The result is called the modln2 data set 1121 (also called the "training set"), which contains the fraud-related variables needed to train the net-work. Scaler module 1122 scales the variables such that the mean value for each variable in the scaled training set is 0.0 and the standard deviation is 1.0, to create scaled modln2 data set 1123.
Many fraud-related variables may be generated using variations of the pre-processing method described above. Fraud-related variables used in the preferred embodiment include:
Customer usage pattern profiles representing time-of-day and day-of-week profiles;
Expiration date for the credit card;
Dollar amount spent in each SIC (Standard Tn~lllstri~l Classification) merchant group category during the current day;
Percentage of dollars spent by a customer in each SIC merchant group category during the current day;
Number of transactions in each SIC merchant group category during the current day;
Percentage of number of transactions in each SIC merchant group category during the current day;
Categorization of SIC merchant group categories by fraud rate (high, medium, - or low risk);
Categorization of SIC merchant group categories by customer types (groups of customers that most frequently use certain SIC categories);
Categorization of geographic regions by fraud rate (high, medium, or low risk);
Categorization of geographic regions by customer types;
Mean number of days between transactions;
Variance of number of days between transactions;
Mean time between transactions in one day;
Variance of time between transactions in one day;
Number of multiple transaction declines at same merchant;
Number of out-of-state transactions;
Mean number of transaction declines;
Year-to-date high balance;
Transaction amount;
Transaction date and time;
Transaction type.
WO 94/06103 ~ PCr/US93/08400 21~ g -10- --Additional fraud-related variables which may also be considered are listed below:
Current Day Cardholder Fraud Related Variables bweekend - current day boolean in~ ting current datetime considered weekend cavapvdl - current day mean dollar amount for an a~plov~l cavapvdl - current day mean dollar amount for an approval cavaudl - current day mean dollars per auth across day ccoscdom - current day cosine of the day of month i.e. cos(day ((datepart(cst_dt) *
&TWOPI)/30));
ccoscdow - current day cosine of the day of week i.e. cos(weekday((datepart(cst_dt) *
&TWOPI)/7));
ccoscmoy - current day cosine of the month of year i.e. cos(month ((datepart(cst_dt) *
&TWOPI)/12));
cdom - current day day of month cdow - current day day of week chdzip - current cardholder zip chibal - current day high balance chidcapv - current day highest dollar amt on a single cash approve chidcdec - current day highest dollar amt on a single cash decline chidmapv - current day highest dollar amt on a single merch approve chidmdec - current day highest dollar amt on a single merch decline chidsapv - current day highest dollar amount on a single approve chidsau - current day highest dollar amount on a single auth chidsdec - current day highest dollar amount on a single decline cmoy - current day month of year cratdcau - current day ratio of declines to auths csincdom - current day sine of the day of month i.e. sin(day ((datepart(cst_dt) * &TWOPI)/30));
csincdow - current day sine of the day of week i.e. sin(weekday((datepart(cst_dt) *
&TWOPI)/7));
csincmoy - current day sine of the month of year i.e. sin(month ((datepart(cst_dt) *
&TWOPI)/12));
cst_dt - current day cst datetime derived from zip code and CST auth time ctdapv - current day total dollars of approvals ctdau - current day total dollars of auths ctdcsapv - current day total dollars of cash advance approvals ctdcsdec - current day total dollars of cash advance declines WO 94/06103 ~! t4 4 ~6 ~ PCI/US93/08400 ctddec - current day total dollars of declines ctdmrapv - current day total dollars of merch~n~i~e approvals ctdmrdec - current day total dollars of merch~n(li~e declines ctnapv - current day total number of approves ctnau - current day total number of auths ctnaulOd - current day number of auths in day<=$10 ctnaudy - current day total number of auths in a day ctncsapv - current day total number of cash advance approvals ctncsapv - current day total number of cash approves ctncsdec - current day total number of cash advance declines ctndec - current day total number of declines c~ - current day total number of merchandise approvals ctnmrdec - current day total number of merchandise declines ctnsdapv - current day total number of approvals on the same day of week as current day ctnwdaft - current day total number of weekday afternoon approvals ctnwdapv - current day total number of weekday approvals ctnwdeve - current day total number of weekday evening approvals ctnwdmor - current day total number of weekday morning approvals ctnwdnit - current day total number of weekday night approvals ctnweaft - current day total number of weekend afternoon approvals ctnweapv current day total number of weekend approvals ctnweeve current day total number of weekend evening approvals ctnwemor current day total number of weekend morning approvals ctnwenit current day total number of weekend night approvals currbal current day current balance cvr:mdl current day variance of dollars per auth across day czrate 1 current day zip risk group 1 'Zip very high fraud rate' czrate2 current day zip risk group 2 'Zip high fraud rate' czrate3 current day zip risk group 3 'Zip medium high fraud rate' czrate4 current day zip risk group 4 'Zip medium fraud rate' czrateS current day zip risk group S 'Zip medium low fraud rate' czrate6 current day zip risk group 6 'Zip low fraud rate' czrate7 current day zip risk group 7 'Zip very low fraud rate' czrate8 current day zip risk group 8 'Zip unknown fraud rate' ctdsfaO 1 current day total dollars of transactions in SIC factor group 01 ctdsfaO2 current day total dollars of transactions in SIC factor group 02 ctdsfaO3 current day total dollars of transactions in SIC factor group 03 W094/06l03 21440~8 - 12- PCI/US93/08400 ctdsfaO4 current day total dollars of trzln~c.tinns in SIC factor group 04 ctdsfaO5 current day total dollars of transactions in SIC factor group 05 ctdsfaO6 current day total dollars of transactions in SIC factor group 06 ctdsfaO7 current day total dollars of transactions in SIC factor group 07 ctdsfaO8 current day total dollars of transactions in SIC factor group 08 ctdsfaO9 current day total dollars of transactions in SIC factor group 09 ctdsfalO current day total dollars of transactions in SIC factor group lO
ctdsfal l current day total dollars of transactions in SIC factor group l l ctdsraOl current day total dollars of transactions in SIC fraud rate group Ol ctdsraO2 current day total dollars of transactions in SIC fraud rate group 02 ctdsraO3 current day total dollars of transactions in SIC fraud rate group 03 ctdsraO4 current day total dollars of transactions in SIC fraud rate group 04 ctdsraO5 current day total dollars of transactions in SIC fraud rate group 05 ctdsraO6 current day total dollars of transactions in SIC fraud rate group 06 ctdsraO7 current day total dollars of transactions in SIC fraud rate group 07 ctdsvaO l current day total dollars in SIC VISA group O l ctdsvaO2 current day total dollars in SIC VISA group 02 ctdsvaO3 current day total dollars in SIC VISA group 03 ctdsvaO4 current day total dollars in SIC VISA group 04 ctdsvaO5 current day total dollars in SIC VISA group 05 ctdsvaO6 current day total dollars in SIC VISA group 06 ctdsvaO7 current day total dollars in SIC VISA group 07 ctdsvaO8 current day total dollars in SIC VISA group 08 ctdsvaO9 current day total dollars in SIC VISA group 09 ctdsvalO current day total dollars in SIC VISA group lO
ctdsval l current day total dollars in SIC VISA group l l ctnsfaOl current day total number of transactions in SIC factor group Ol ctnsfaO2 current day total number of transactions in SIC factor group 02 ctnsfaO3 current day total number of transactions in SIC factor group 03 ctnsfaO4 current day total number of transactions in SIC factor group 04 ctnsfaO5 current day total number of transactions in SIC factor group 05 ctnsfaO6 current day total number of transactions in SIC factor group 06 ctnsfaO7 current day total number of transactions in SIC factor group 07 ctnsfaO8 current day total number of transactions in SIC factor group 08 ctnsfaO9 current day total number of transactions in SIC factor group 09 ctnsfalO current day total number of transactions in SIC factor group lO
ctnsfal l current day total number of transactions in SIC factor group l l WO 94/06103 ;~ ~ 4 4 ~ t~ 8 PCI/US93/08400 ctnsraOl current day total number of transactons in SIC fraud rate group 01 ctnsraO2 current day total number of transactons in SIC fraud rate group 02 ctnsraO3 current day total number of transactons in SIC fraud rate group 03 ctnsraO4 current day total number of transactons in SIC fraud rate group 04 ctnsraO5 current day total number of transactons in SIC fraud rate group 05 ctnsraO6 current day total number of transactons in SIC fraud rate group 06 ctnsraO7 current day total number of transactons in SIC fraud rate group 07 ctnsvaO1 current day total number in SIC VISA group Ol ctnsvaO2 current day total number of SIC VISA group 02 ctnsvaO3 current day total number of SIC VISA group 03 ctnsvaO4 current day total number of SIC VISA group 04 ctnsvaO5 current day total number of SIC VISA group 05 ctnsvaO6 current day total number of SIC VISA group 06 ctnsvaO7 current day total number of SIC VISA group 07 ctnsvaO8 current day total number of SIC VISA group 08 ctnsvaO9 current day total number of SIC VISA group 09 ctnsvalO current day total number of SIC VISA group 10 ctnsval l current day total number of SIC VISA group l l 7 Day Cardholder Fraud Related Variables raudymdy 7 day ratio of auth days over number of days in the window ravapvdl 7 day mean dollar amount for an approval ravaudl 7 day mean dollars per auth across window rddapv 7 day mean dollars per day of a~lov~ls rddapv2 7 day mean dollars per day of approvals on days with auths rddau 7 day mean dollars per day of auths on days with auths rddauall 7 day mean dollars per day of auths on all days in window rddcsapv 7 day mean dollars per day of cash approvals rddcsdec 7 day mean dolalrs per day of cash declines rdddec 7 day mean dollars per day of declines - rdddec2 7 day mean dollars per day of declines on days with auths rddmrapv 7 day mean dollars per day of merchandise approvals rddmrdec 7 day mean dollars per day of merchandise declines rdnapv 7 day mean number per day of approvals rdnau 7 day mean number per day of auths on days with auths rdnauall 7 day mean number per day of auths on all days in window WO 94tO6103 i~ - PCr/US93/08400 2 ~ 8 - 14-rdncsapv 7 day mean number per day of cash approvals r-ln~s-l~c 7 day mean number per day of cash declines rdndec 7 day mean number per day of declines rdnmrapv 7 day mean number per day of merchandise approvals rdnmrdec 7 day mean number per day of merchandise declines rdnsdap2 7 day mean number per day of approvals on same day of week calculated only for those days which had approvals rdnsdapv 7 day mean number per day of approvals on same day of week as current day rdnwdaft 7 day mean number per day of weekday afternoon approvals rdnwdapv 7 day mean number per day of weekday approvals rdnwdeve 7 day mean number per day of weekday evening approvals rdnwdmor 7 day mean number per day of weekday morning approvals rdnwdnit 7 day mean number per day of weekday night approvals rdnweaft 7 day mean number per day of weekend afternoon approvals rdnweapv 7 day mean number per day of weekend approvals rdnweeve 7 day mean number per day of weekend evening approvals rdnwemor 7 day mean number per day of weekend morning approvals rdnwenit 7 day mean number per day of weekend night approvals rhibal 7 day highest window balance rhidcapv 7 day highest dollar amt on a single cash approve rhidcdec 7 day highest dollar amt on a single cash decline rhidmapv 7 day highest dollar amt on a single merch approve rhidmdec 7 day highest dollar amt on a single merch decline rhidsapv 7 day highest dollar amount on a single approve rhidsau 7 day highest dollar amount on a single auth rhidsdec 7 day highest dollar amount on a single decline rhidtapv 7 day highest total dollar amount for an approve in a single day rhidtau 7 day highest total dollar amount for any auth in a single day rhidtdec 7 day highest total dollar amount for a decline in a single day rhinapv 7 day highest number of approves in a single day rhinau 7 day highest number of auths in a single day rhindec 7 day highest number of declines in a single day rnaudy 7 day number of days in window with any auths rnausd 7 day number of same day of week with any auths rnauwd 7 day number of weekday days in window with any auths rnauwe 7 day number of weekend days in window with any auths rncsandy 7 day number of days in window with cash auths W094/06103 ~ a~ Pcr/US93/08400 - 15~
rmnraudy 7 day number of days in window with merchant auths rtdapv 7 day total dollars of approvals rtdau 7 day total dollars of auths rtdcsapv 7 day total dollars of cash advance approvals rtdcsdec 7 day total dollars of cash advance declines rtddec 7 day total dollars of declines rtdmrapv 7 day total dollars of merchandise approvals rtdmrdec 7 day total dollars of merchandise declines rtnapv 7 day total number of approvals rtnapvdy 7 day total number of approves in a day rtnau 7 day total number of auths rtnaulOd 7 day number of auths in window <=$10 rtncsapv 7 day total number of cash advance approvals rtncsdec 7 day total number of cash advance declines rtndec 7 day total number of declines rtnmrapv 7 day total number of merch~n~li.ce approvals rtnmrdec 7 day total number of merchandise declines rtnsdapv 7 day total number of approvals on the same day of week as current dayrtnwdaft 7 day total number of weekday afternoon approvals rtnwdapv 7 day total number of weekday approvals rtnwdeve 7 day total number of weekday evening approvals rtnwdmor 7 day total number of weekday morning approvals rtnwdnit 7 day total number of weekday night approvals rtnweaft 7 day total number of weekend afternoon approvals rtnweapv 7 day total number of weekend approvals rtnweeve 7 day total number of weekend evening approvals rtnwemor 7 day total number of weekend morning approvals rtnwenit 7 day total number of weekend night approvals rvraudl 7 day variance of dollars per auth across window Profile Cardholder Fraud Related Variables paudymdy - profile ratio of auth days over number of days in the month - pavapvdl - profile mean dollar amount for an approval pavaudl - profile mean dollars per auth across month pchdzip - profile the last zip of the cardholder pdbm - profile value of 'date became member' at time of last profile update WO 94/06103 PCr/US93/08400 ~14~068 -16- ~
pddapv - profile daily mean dollars of approvals pddapv2 - profile daily mean dollars of approvals on days with auths pddau - profile daily mean dollars of auths on days with auths pddau30 - profile daily mean dollars of auths on all days in month pddcsapv - profile daily mean dollars of cash approvals pddcsdec - profile daily mean dollars of cash declines pdddec - profile daily mean dollars of declines pdddec2 - profile daily mean dollars of declines on days with auths pddmrapv - profile daily mean dollars of merchandise approvals pddmrdec - profile daily mean dollars of merchandise declines pdnapv - profile daily mean number of approvals pdnau - profile daily mean number of auths on days with auths pdnau30 - profile daily mean number of auths on all days in month pdncsapv - profile daily mean number of cash approvals pdncsdec - profile daily mean number of cash declines pdndec - profile daily mean number of declines pdnmrapv - profile daily mean number of merchandise approvals pdnmrdec - profile daily mean number of merchandise declines pdnwlap2 - profile mean number of approvals on Sundays which had auths pdnwlapv - provilde mean number of approvals on Sundays (day 1 of week) pdnw2ap2 - profile mean number of approvals on Mondays which had auths pdnw2apv - profile mean number of approvals on Mondays (day 2 of week) pdnw3ap2 - profile mean number of approvals on Tuesdays which had auths pdnw3apv - profile mean number of approvals on Tuesdays (day 3 of week) pdnw4ap2 - profile mean number of approvals on Wednesdays which had auths pdnw4apv - profile mean number of approvals on Wednesdays (day 4 of week) pdnwSap2 - profile mean number of approvals on Thursdays which had auths pdnwSapv - profile mean number of approvals on Thursdays (day 5 of week) pdnw6ap2 - profile mean number of approvals on Fridays which had auths pdnw6apv - profile mean number of approvals on Fridays (day 6 of week) pdnw7ap2 - profile mean number of approvals on Saturdays which had auths pdnw7apv - profile mean number of approvals on Saturdays (day 7 of week) pdnwdaft - profile daily mean number of weekday afternoon approvals pdnwdapv - profile daily mean number of weekday approvals pdnwdeve - profile daily mean number of weekday evening approvals pdnwdmor - profile daily mean number of weekday morning approvals pdnwdnit - profile daily mean number of weekday night approvals ~4~8 WO 94/06103 PCr/US93/08400 pdnweaft - profile daily mean number of weekend afternoon approvals pdnweapv - profile daily mean number of weekend approvals pdnweeve - profile daily mean number of weekend evening approvals pdnwemor - profile daily mean number of weekend morning approvals pdnwenit - profile daily mean number of weekend night approvals pexpir - profile expiry date stored in profile; update if curr date>pexpir phibal - profile highest monthly balance phidcapv - profile highest dollar amt on a single cash approve in a month phidcdec - profile highest dollar amt on a single cash decline in a month phidmapv - profile highest dollar amt on a single merch approve in a month phidmdec - profile highest dollar amt on a single merch decline in a month phidsapv - profile highest dollar amount on a single approve in a month phidsau - profile highest dollar amount on a single auth in a month phidsdec - profile highest dollar amount on a single decline in a month phidtapv - profile highest total dollar amount for an approve in a single day phidtau - profile highest total dollar amount for any auth in a single day phidtdec - profile highest total dollar amount for a decline in a single day phinapv - profile highest number of approves in a single day phinau - profile highest number of auths in a single day phindec - profile highest number of declines in a single day pmlavbal - profile average bal. during 1st 10 days of mo.
pmlnauths - profile number of auths in the 1st 10 days of mo.
pm2avbal - profile average bal. during 2nd 10 days of mo.
pm7n~uthc - profile number of auths in the 2nd 10 days of mo.
pm3avbal - profile average bal. during rem~ining days pm3nauths - profile number of auths in the last part of the month.
pmovewt - profile uses last zip to ~let.q.rmine recent residence move; pmovewt=2 for a move within the previous calendar month; pmovew pnaudy - profile number of days with auths pnauw 1 - profile number of Sundays in month with any auths pnauw2 - profile number of Mondays in month with any auths pnauw3 - profile number of Tuesdays in month with any auths pnauw4 - profile number of We-lnes~l~ys in month with any auths pnauwS - profile numberof Thursdays in month with any auths pnauw6 - profile number of Fridays in month with any auths pnauw7 - profile number of Saturdays in month with any auths pnauwd - profile number of weekday days in month with any auths 2 ~ 8 - 18 -pnauwe - profile number of weekend days in month with any auths pncsaudy - profile number of days in month with cash auths pnmraudy - profile number of days in month with merchant auths pnweekday - profile number of weekday days in the month pnweekend - profile number of weekend days in the month pratdcau - profile ratio of declines to auths profage - profile number of months this account has had a profile (up to 6 mo.) psdaudy - profile standard dev. of # days between transactions in a month psddau - profile standard dev. of $ per auth in a month ptdapv - profile total dollars of approvals in a month ptdau - profile total dollars of auths in a month ptdaudy - profile total dollars of auths in a day ptdcsapv - profile total dollars of cash advance approvals in a month ptdcsdec - profile total dollars of cash advance declines in a month ptddec - profile total dollars of declines in a month ptdmrapv - profile total dollars of merchandise approvals in a month ptdmrdec - profile total dollars of merchandise declines in a month ptdsfaOl - profile total dollars of transactions in SIC factor group 01 ptdsfaO2 - profile total dollars of transactons in SIC factor group 02 ptdsfaO3 - profile total dollars of transactions in SIC factor group 03 ptdsfaO4 - profile total dollars of transactions in SIC factor group 04 ptdsfaO5 - profile total dollars of transactions in SIC factor group 05 ptdsfaO6 - profile total dollars of transactions in SIC factor group 06 ptdsfaO7 - profile total dollars of transactions in SIC factor group 07 ptdsfaO8 - profile total dollars of transactions in SIC factor group 08 ptdsfaO9 - profile total dollars of transactions in SIC factor group 09 ptdsfalO - profile total dollars of transactions in SIC factor group 10 ptdsfal 1 - profile total dolalrs of transactions in SIC factor group 11 ptdsraO1 - profile total dollars of transactions in SIC fraud rate group 01 ptdsraO2 - profile total dollars of transactions in SIC fraud rate group 02 ptdsraO3 - profile total dollars of transactions in SIC fraud rate group 03 ptdsraO4 - profile total dollars of transactions in SIC fraud rate group 04 ptdsraO5 - profile total dollars of transactions in SIC fraud rate ~roup 05 ptdsraO6 - profile total dollars of transactions in SIC fraud rate group 06 ptdsraO7 - profile total dollars of transactions in SIC fraud rate group 07 ptdsvaO1 - profile total dollars in SIC VISA group 01 ptdsvaO2 - profile total dollars in SIC VISA group 02 WO 94/06103 2 1 4 ~ 0 6 ~ PCrtUS93/08400 .
ptdsvaO3 - profile total dollars in SIC VISA group 03 ptdsvaO4 - profile total dollars in SIC VISA group 04 ptdsvaO5 - profile total dollars in SIC VISA group 05 ptdsvaO6 - profile total dollars in SIC VISA group 06 ptdsvaO7 - profile total dollars in SIC VISA group 07 ptdsvaO8 - profile total dollars in SIC VISA group 08 ptdsvaO9 - profile total dollars in SIC VISA group 09 ptdsvalO - profile total dollars in SIC VISA group 10 ptdsval 1 - profile total dollars in SIC VISA group 11 ptnapv - profile total number of approvals in a month ptnapvdy - pro~lle total number of approves a day ptnau - profile total number of auths in a month ptnaulOd - profile number of auths in monthc=$10 ptnaudy - profile total number of auths in a day ptncsapv - profile total number of cash advance approvals in a month ptncsdec - profile total number of cash advance declines in a month ptndec - profile total number of declines in a month ptndecdy - profile total number of declines in a day ptnmrapv - profile total number of merchandise approvals in a month ptnmrdec - profile total number of merch~n~ e declines in a month ptnsfaO1 - profile total number of transactions in SIC factor group 01 ptnsfaOl - profile total number of transactions in SIC factor group 02 ptnsfaO3 - profile total number of transactions in SIC factor group 03 ptnsfaO4 - profile total number of transactions in SIC factor group 04 ptnsfaO5 - profile total number of transactions in SIC factor group 05 ptnsfaO6 - profile total number of transactions in SIC factor group 06 ptnsfaO7 - profile total number of transactions in SIC factor group 07 ptnsfaO8 - profile total number of transactions in SIC factor group 08 ptnsfaO9 - profile total number of transactions in SIC factor group 09 ptnsfalO - profile total number of transactions in SIC factor group 10 ptnsfal 1 - profile total number of transactions in SIC factor group 11 ptnsraOl - profile total number of transactions in SIC fraud rate group 01 ptnsraO2 - profile total number of transactions in SIC fraud rate group 02 ptnsraO3 - profile total number of transactions in SIC fraud rate group 03 ptnsraO4 - profile total number of transactions in SIC fraud rate group 04 ptnsraO5 - profile total number of transactions in SIC fraud rate group 05 ptnsraO6 - profile total number of transactions in SIC fraud rate group 06 7 ~
WO 94/06103 ~ E, ~ PCI/US93/08400 2~4~68 -20- --ptnsraO7 - profile total number of transactions in SIC fraud rate group 07 ptnsvaOl - profile total number in SIC VISA group 01 ptnsvaO2 - profile total number in SIC VISA group 02 ptnsvaO3 - profile total number in SIC VISA group 03 ptnsvaO4 - profile total number in SIC VISA group 04 ptnsvaO5 - profile total number in SIC VISA group 05 ptnsvaO6 - profile total number in SIC VISA group 06 ptnsvaO7 - profile total number in SIC VISA group 07 ptnsvaO8 - profile total number in SIC VISA group 08 ptnsvaO9 - profile total number in SIC VISA group 09 ptnsvalO - profile tot~l number in SIC VISA group 10 ptnsva 11 - profile total number in SIC VISA group 11 ptnwlapv - profile total number of a~pr~vals on Sundays (day l of week) ptnw2apv - profile total number of a~p,ovals on Mondays (day 2 of week) ptnw3apv - profile total number of approvals on Tuesdays (day 3 of week) ptnw4apv - profile total number of approvals on Wednesdays (day 4 of week) ptnw5apv - profile total number of approvals on Thursdays (day 5 of week) ptnw6apv - profile total number of a~plovals on Fridays (day 6 of week) ptnw7apv - profile total number of approvals on Saturdays (day 7 of week) ptnwdaft - profile total number of weekday afternoon approvals in a month ptnwdapv - profile total number of weekday approvals in a month ptnwdeve - profile total number of weekday evening approvals in a month ptnwdmor - profile total number of weekday morning approvals in a month ptnwdnit - profile total number of weekday night approvals in a month ptnweaft - profile total number of weekend afternoon approvals in a month ptnweapv - profile total number of weekend approvals in a month ptnweeve - profile total number of weekend evening approvals in a month ptnwemor - profile total number of weekend morning approvals in a month ptnwenit - profile total number of weekend night approvals in a month pvdaybtwn - profile variance in number of days between trx's (min of 3 trx) pvraudl - profile variance of dollars per auth across month MERCHANT FRAUD VARIABLES
mtotturn Merchant Total turnover for this specific merchant msicturn Merchant Cum~ tive SIC code turnover mctrtage Merchant Contract age for specific merchant WO 94/06103 ~ 1 4 4 ~ 6 ~ PCI`/US93/08400 m~slg.sjc Merchant Average contract age for this SIC code mavgnbtc Merchant Average number of transactions in a batch m~mttrX Merchant Average amount per transaction (average amount per authorization) mvaramt Merchant Variance of amount per transaction mavgtbtc Merchant Average time between batches mavgtaut Merchant Average time between authorizations for this merchant mratks Merchant Ratio of keyed versus swiped transactions mnidclac Merchant Number of identical customer accounts mnidcham Merchant Number of identical charge amounts mtrxsrc Merchant What is the source of transaction (ATM, merchant, etc.) ll~Ll~,~ Merchant How is the transaction transported to the source (terminal, non-terminal, voice authorization) mfloor Merchant Floor limit mchgbks Merchant Charge-backs received mrtrvs Merchant Retrievals received (per SIC, merchant, etc.). The issuer pays for a retrieval.
macqrat Merchant Acquirer risk management rate (in Europe one merchant can havemultiple acquirers, but they dont have records about how many or who.) mprevrsk Merchant Previous risk management at this merchant? Yes or No mtyprsk Merchant Type of previous risk management (counterfeit, multiple imprint, lost/stolen/not received) msicrat Merchant SIC risk management rate mpctaut Merchant Percent of transactions authorized Network Training: Once pre-procescing is complete, the fraud-related variables are fed to the network and the network is trained. The preferred embodiment uses a modeling technique known as a "feed forward" neural network. This type of network estim~t~s parameters which define relationships among variables using a training method. The preferred training method, well known to those skilled in the art, is called "backpropagation gradient descent optimi~tion~
although other well-known neural network training techniques may also be used.
One problem with neural n~Lwolh~, built with conventional backpropagation methods is - insufficient generalizability. Generalizability is a measure of the predictive value of a neural network. The attempt to m~ximi7e generalizability can be interpreted as choosing a network model with enough complexity so as not to underfit the data but not too much complexity so as to overfit the data. One measure of the complexity of a network is the number of hidden processing elements, so that the effort to maximize generalizability translates into a selection among models having different numbers of hidden processing elements. Unfortunately, it is 2~0~8 -22-often not possible to obtain all the nonlinearity required for a problem by adding hidden proce~cing elements without introducing excess complexity. Many weights that come with the addition of each new hidden proces~ing element may not be required or even helpful for the modeling task at hand. These excess weights tend to make the network fit the idiosyncrasies or "noise" of the data and thus fail to generalize well to new cases. This problem, known as overfitting, typically arises because of an excess of weights.
Weight decay is a method of developing a neural network that minimi7los overfitting without sacrificing the predictive power of the model. This method initially provides the network with all the nonlinearity it needs by providing a large number of hidden procçscing elements. Subsequently, it decays all the weights to varying degrees so that only the weights that are nPcçcs~ry for the approximation task remain. Two central premises are employed: 1 ) when given two models of equivalent performance on a training data set, favor the smaller model; and 2) implement a cost function that penalizes complexity as part of the backpropaga-tion algo~ l.. The network is trained by minimi7ing this cost function. Complexity is only justified as it expresses information contained in the data. A weight set that embodies all or almost all of the information in the data and none of the noise will maximize generalizability and perform~n~e The cost function is constructed by introducing a "decay term" to the usual error function used to train the network. It is clesign~d to optimize the model so that the network captures all the important information in the training set, but does not adapt to noise or random characteristics of the training set. In view of these requirements, the cost function must take into account not only prediction error, but also the signific~nce of model weights. A combination of these two terms yields an objective function which, when minimi7~d, generalizes optimally.
Performing a conventional gradient descent with this objective function optimizes the model.
In introducing the decay term, an assumption is made about what constitutes information.
The goal is to choose a decay term that accurately hypothesizes the prior distribution of the weights. In finding a good prior distribution, one examines the likelihood that the weights will have a given distribution without knowledge of the data.
Weigend et al, "Generalization by Weight-Flimin~tion with Application to Forecasting", in Advances in Neural Information Processing Systems 3~ pp. 875-82, and incorporated herein by reference, discloses the following cost function for weight decay:
arget~--output~,) +~ +~l~ 2/~ 2 ( where:
D is the data set;
targetk iS the target, or desired, value for element k of the data set;
WO 94/06103 ~ Pcr/US93/0840o 23 ~
outputk iS the network output for element k of the data set;
I represents the relative importance of the complexity term;
W is the weight set;
wl is the value of weight i; and wO is a constant that controls the shape of the curve that penalizes the weights.
The first term of the Weigend function measures the perforrnance of the network, while the second term measures the complexity of the network in terms of its size. With this cost function, small weights decay rapidly, while large weights decay slowly or not at all.
A major failing of the Weigend cost function, and similar weight decay schemes, is that they do not accurately mimic the intenlle~l prior distribution. Finding a good prior distribution (or 'prior") is a key element to developing an effective model. Most of the priors in the literature are sufficient to demonstrate the concept of weight decay but lack the strengths required to accommodate a wide range of problems. This occurs because the priors tend to decay weights evenly for a given procescing element, without sufficiently distinguishing important weights (which contain more information) from uni~ o, lallt weights (which contain less information). This often results either in 1) undesired decaying of important weights, which (liminiches the power of the system to accommodate nonlinearity, or 2) undesired retention of excess unimportant weights, which leads to overfitting.
The present invention uses the following improved cost function, which addresses the above problems:
--~ (targetk--outputk )2 + gl~, (ClWi2 ~ J (Eq. 2) 2 keD ieW 1 + ¦ Wj l where g represents a new term known as the interlayer gain multiplier for the decay rate, and c, is a constant. The interlayer gain multiplier takes into account the relative proximity of the weights to the input and output ends of the network. Thus, the interlayer gain multiplier al-lows application of the decay term with greater potency to elements that are closer to the inputs, where the majority of the weights typically reside, while avoiding excessive decay on weights corresponding to elements closer to the outputs, which are more critical, since their elimin~tion can effectively sever large numbers of input-side weights.
By intensifying decay on input-side elements, the cost function of Equation 2 improves the ability of model development component 801 to decay individual weights while preserving proces~ing elements cont~ining valuable information. The result is that weak interactions are elimin~tto-l while valid interactions are retained. By retaining as many processing elements as possible, the model does not lose the power to model nonlinearities, yet the ovçrfittin~ problem is reduced because unnecessary individual weights are removed.
WO 94/06103 PCI/US~3/08400 2~4~8 -24-Once the cost function has been iteratively applied to the network, weights that have been decayed to a very small number (defined as e) are removed from the network. This step, known as "thresholding the net" is pelr~lll.ed because it is often difficult to completely decay weights to zero.
Once the network has been trained using past data, the network's model definition is stored in data files. One portion of this definition, called the "CFG" file, specifies the parameters for the network's input variables, including such information as, for example, the lengths of the variables, their types, and their ranges. Referring now to Figure 21, there is shown a portion of a typical CFG file, specifying parameters for an ACCOUNT variable 2101 (representing a customer account number) and a PAUDYMDY variable 2102 (a profile variable representing the ratio of transaction days divided by the number of days in the month).
The file formats used to store the other model definition files for the network are shown below.
ASCII File Formats The ASCII network data files (.cta, .sta, .Ica, .wta) consist of tokens (non-whitespace) separated by whitespace (space, tab, newline).
Whitespace is ignored except to separate tokens. Use of line breaks and tabs is encouraged for clarity, but otherwise irrelevant.
File format notation is as follows:
* Br~ck~te~l text denotes a token.
* Nonbracketed text denotes a literal token which must be matched exactly, including case.
* Comments on the right are not part of the file format; they simply provide further description of the format.
* In the comments, vertical lines denote a block which can be repeated. Nested veltical lines denote repeatable sub-blocks.
21~6~
.cta Format Fileformat Comments cts <NetName>
<Value> I Repeated as needed cts and <NetName> must appear first. <NetName>is the standard abbreviation, lowercase (e.g., mbpn). The <Value>s are the network constants values, in the order defined within the constants structure. If a constants value is an array or structured type, each element or field must be a separate token, appearing in the proper order.
Example Comrnents cts mbpn 2 InputSize OutputSize cHidSlabs 2 HiddenSize[0]
o HiddenSize[ 1 ]
0 HiddenSize[2]
3 p~ntlom.Seed 1 . 0 InitWeightMax 0 WtsUpdateFlag 0 ConnectInputs 0 FnClass 1. 0 Parml 1 . 0 Parm2 -1 . 0 Parm3 0 . 0 Parm4 0 . 0 Parm5 cEntTbl xLow 0 . 1 xHigh O . 2 HiddenAlpha[0]
O . O HiddenAlpha[1]
Wo 94/06103 ~ PCI/US93/08400 21~68 -26- ~
Example Comments 0 . 0 HiddenAlpha[2]
0 . 1 OutputAlpha 0 . 9 HiddenBeta[0]
0 . 0 HiddenBeta[l]
0 . 0 HiddenBeta[2]
0 . 9 OutputBeta 0 . 0 Tolerance 0 WtsUpdateFlag 0 BatchSize 0 LinearOutput 0 ActTblFlag StatsFlag LearnFlag In this example, HiddenSize, HiddenAlpha, and HiddenBeta are all arrays, so each element (0, l, 2) has a separate token, in the order they appear in the type.
.sta Format File format Cornments sts <NetName>
<cSlab>
<nSlab> I Repeated cSlab times <cPe>
<state> I I RepeatedcPetimes sts and <NetName> must appear first. <NetName>is the standard abbreviation, lower-case. <cSlab>is a count of the slabs which have states stored in the file. The remainder of the file consists of cSlab blocks, each describing the states of one slab. The order of the slab blocks in the file is not hlll)olL~lt.<nslab>is the slab number, as defined in the ~ .h file. cPeis the number of states for the slab. <state>is the value of a single state. If the state type is an array or structured type, each element or field must be a separate token, appearing in the proper order. There should be cPe <state> values in the slab bloclc.
WO 94/06103 214 4 ~ G 8 PCr/US93/08400 13xample Comments sts mbpn 6 cSlab O nSlab -- SlabInMbpn 2 cPeIn O . O StsIn[O]
O . O StsIn[1]
nSlab -- SlabTrnMbpn cPeTrn O . O StsTrn[O]
2 nSlab -- SlabHidOMbpn 2 cPeHidO
O . O StsHidO[O]
O . O StsHidO[l]
nSlab -- SlabOutMbpn cPeOut O . O StsOut[O]
6 nSlab -- SlabBiasMbpn cPeBias 1 . o StsBias[O]
7 nSlab -- SlabStatMbpn 3 cPeStat O . O StsStat[O]
O . O StsStat[1]
O O StsStat[2 i .Ica Format File format Comments lcl <NetName>
<cSlab>
<nSlab> I Repeated cSlab times <cPe>
<local> i I Repeated cPe times WO 94/06103 ' ~ PCI/US93/08400 ~1~4~68 -28-The .Ica format is just like the .sta format except that sts is replaced by lcl. lcl and <NetName> must appear first. <NetName>is the standard abbreviation, lowercase.
<cSlab>is a count of the slabs which have local data stored in the file. The rem~inder of the file consists of cSlab blocks, each describing the local data values of one slab.
<nSlab>is the slab number, as defined in the ~x.h file. The order of the slab blocks in the file is not important. cPeis the number of local data values for the slab. <local> is the value of a single local data element. If the local data type is an array or structured type, each element or field must be a separate token, appearing in the proper order. There should be cPe <local> values in the slab block.
Fxample Comments lcl mbpn 3 cSlab 2 nSlab -- SlabHidOMbpn 2 cPe 0.0 LclHidO[O].Error 0.0 LclHidO[O].NetInp 0.0 LclHidO[ 1 ] .Error 0.0 LclHidO[l].NetInp nSlab -- SlabOutMbpn cPe 0.0 LclOut[O].Error 0.0 LclOut[O].NetInp 7 nSlab -- SlabStatMbpn 3 cPe O LclStat[O].cIter 0.0 LclStat[O].Sum O LclStat[l].cIter 0.0 LclStat[l].Sum O LclStat[2].cIter 0.0 LclStat[2].Sum In this example, the <local> values are all structured types, so each field (Error and NetInp; cIter and Sum) has a separate token, in the order they appear in the type.
WO 94/06103 2 1 ~ 8 Pcr/US93/08400 .wta Format File formatComments wts <NetName>
<cClass>
<nSlab> I Repeated cClass times <nClass>
<cIcn>
<weight> I I RepeatedcIcntimes wts and <NetName> must appear first. <NetName>is the standard abbreviation, lowercase. <cClass>is a count of the slab/class combinations which have weights stored in the file. The remainder of the file consists of cClass blocks, each describing the weights of one slab. The order of the class blocks in the file is not important. <nSlab>is the slab number, as defined in the ~x.h file. <nClass>is the class number, as defined in the ~x.h file. ~weight>is the value of a single weight. If the weight type is an array or structured type, each element or field must be a separate token, appearing in the proper order. There should be cIcn <weight> values in the slab block.
Example Comments wts mbpn 2 cClass 2 nSlab -- SlabHidOMbpn O nClass -- PeHidOMbpnFromPrev cIcn O . O WtsHidO[PE_O][O]
0 . O WtsHidO[PE_O][l]
O . O WtsHidO[PE_0]~2~
O . O WtsHidO[PE_l][O]
O . 0 WtsHidO[PE_l][l]
O . 0 WtsHidO[PE_1][2]
nSlab -- SlabOutMbpn O nClass -- PeOutMbpnFromPrev 3 cIcn O . O WtsOut[PE_O][O]
WO 94/06103 ~ ~ ~ PCI/US93/08400 21~4~68 ~30-Fxample Comments 0 . 0 WtsOut[PE_0][1]
O . 0 WtsOut[PE_0][2]
Weights values for a slab and class are stored as a one-~lim~n~ional array, but conceptually are indexed by two values -- PE and interconnect within PE. The values are stored in row-major order, as exemplified here.
Transaction Processing Component 802 Once the model has been created, trained, and stored, fraud detection may begin.Transaction processing component 802 of system 100 preferably runs within the context of a conventional authorization or posting system for customer transactions. Transaction processing component 802 reads current transaction data and customer data from databases 805, 806, and generates as output fraud scores representing the likelihood of fraud for each transaction.
Furthermore, transaction proces~ing component 802 can compare the likelihood of fraud with a predetermined threshold value, and flag transactions for which the threshold is exceeded.
The current transaction data from ~t~h~e 805 typically includes information such as:
transaction dollar amount; date; time (and time zone if necessary); approve/decline code;
cash/merch~n~ e code; available credit (or balance); credit line; merchant category code;
merchant ZIP code; and P~ v~.rifi~tion (if applicable).
The customer data from database 806 typically includes information from three sources:
1) general information on the customer; 2) data on all approved or declined transactions in the previous seven days; and 3) a profile record which contains data describing the customer's transactional pattern over the last six months. The general information on the customer typically includes information such as: customer ZIP code; account open date; and expiration date. The profile record is a single record in a profile database summarizing the customer' s transactional pattern in terms of moving averages. The profile record is updated periodically (usually monthly) with all of the transactions from the period for the customer, as described below.
System 100 can operate as either a batch, semi-real-time, or real-time system. The structure and proce~ing flow of each of these variations will now be described.
Batch System: Figure 14 shows operation of a batch system. Transactions are recorded throughout the day or other convenient period 1402. At the end of the day, the system performs steps 1403 to 1409 for each transaction. It obtains data describing the current transaction 1403, as well as past transaction data, customer data, and profile data 1404. It then applies this data to the neural network 1405 and obtains a fraud score 1406. If the fraud score exceeds a threshold 1407, the account is flagged 1408. In the batch system, therefore, the transaction which yielded the high fraud score cannot itself be blocked; rather, the account is flagged 1404 at the end of the day so that no future transactions are possible. Although the batch system does not permit WO 94/06103 2~sl4 ~ 6 8 PCr/US93/08400 immediate detection of fr~n~l~-lçnt transactions, response-time constraints may m~n~l~te use of the batch system in some implementations.
Semi-Real-Time System: The semi-real-time system operates in a similar manner to the batch system and uses the same data files, but it ensures that no more than one high-scoring transaction is authorized before flagging the account. In this system, as shown in Figure 15, fraud likelihood determin~tion is performed (steps 1504 to 1509) immediately after the transaction is authorized 1503. Steps 1504 to 1509 correspond to steps 1403 to 1409 of the batch system illustrated in Figure 14. If the likelihood of fraud is high, the account is flagged 1509 so that no future transactions are possible. Thus, as in the batch system, the current trans-action cannot be blocked; however, the serni-real-time system allows subsequent transactions to be blocked.
Real-Time System: The real-time system performs fraud likelihood determination before a transaction is authorized. Because of response-time constraints, it is preferable to minimi7e the number of ~l~t~b:~e access calls when using the real-time system. Thus, in this embodiment, all of the customer information, including general information and past transaction data, is found in a single record of profile database 806. Profile ~l~t~ba~e 806 is generated from past transaction and customer data before the transaction proces~ing component starts operating, and is updated after each transaction, as described below. Because all needed data are located in one place, the system is able to retrieve the data more quickly than in the batch or semi-real-time schemes. In order to keep the profile database 806 current, profile records are updated, using moving averages where applicable, after each transaction.
Referring now to Figure 16, there is shown a flowchart of a real-time system using the profile database. Upon receiving a merchant's request for authorization on a transaction 1602, the system obtains data for the current transaction 1603, as well as profile data snmm~ri7ing transactional patterns for the customer 1604. It then applies this data to the stored neural network model 1605. A fraud score (representing the likelihood of fraud for the transaction) is obtained 1606 and compared to a threshold value 1607. Steps 1601 through 1607 occur before a transaction is authorized, so that the fraud score can be sent to an authorization system 1608 and the transaction blocked by the authorization system if the threshold has been exceeded. If the threshold is not exceeded, the low fraud score is sent to the authorization system 1609. The system then updates customer profile database 806 with the new transaction data 1610. Thus, in this system, profile database 806 is always up to date (unlike the batch and semi-real-time sysiems, in which profile database 806 is updated only periodically).
Referring now to Figure 12, there is shown the method of creating a profile record. The system performs the steps of this method when there is no çxi~ting profile record for the customer. The system reads the past transaction (l~t~h~ce 1101 for the past six months and the customer database 1103 (steps 1202 and 1203 respectively). It generates a new profile record WO 94/06103 ~ PCr/US93/08400 1204 with the obtained data and saves it in the profile database 1205. If there are more accounts to be processed 1206, it repeats steps 1202 through 1205.
Referring now to Figure 13, there is shown the method of updating an existing profile record. The system reads the past transaction database 1101 for the past six months, customer database 1103 and profile database (steps 1302, 1303, and 1304 respectively). It combines the data into a single value for each variable in the profile database. This value is generated using one of two formulas.
For variables that represent average values over a period of time (for example, mean dollars of transactions in a month), Equation 3 is used:
newProfData = ((1 - a) * oldProfData) + (a * currentVal)) (Eq. 3) For variables that represent extreme values over a period of time (for example, highest monthly balance), Equation 4 is used:
newProfData = max(currentVal, b * oldProfData) (Eq. 4) In Equations 3 and 4:
newProfData is the new value for the profile variable;
oldProfData is the old value for the profile variable;
currentVal is the most recent value of the variable, from the past transaction database;
and a and b are decay factors which are used to give more importance to recent months and less importance to months further in the past.
The value of b is set so that older data will "decay" at an acceptable rate. A typical value for b is 0.95.
The value of a is generated as follows: For the batch and semi-real-time systems, a is set to a value such that the contribution of the value from more than six months previous is nearly zero. For profiles that have been in existence for at least six months, the value of a is 1/6.
For newer prof1les, the value is 1/(n+1), where n is the number of months since the profile was created. For the real-time system, profile updates do not occur at regular intervals. Therefore, a is deterrnined using the following equation:
a = 1 - exp (-t/T) (Eq. 5) where:
t is the time between the current transaction and the last transaction; and -Wo 94/06103 2 ~ 6 8 PCI`/US93/08400 - 33 - , -T is a time constant for the specific variable.
Furthermore, for the real-time system, currentVal represents the value of the variable estimated solely using information related to the current transaction and the time since the last transaction, without reference to any other historical information.
Once the new values for the profile variables have been generated, they are placed in an updated prof1le record 1305 and saved in the profile d~t~h~ce. 1306. If there are more accounts to be processed 1307, the system repeats steps 1302 through 1306.
In all of these embodiments, the current transaction data and the customer data are preferably pre-processed to derive fraud-related variables which have been empirically determined to be effective predictors of fraud. This is done using the same technique and the same fraud-related variables as described above in connection with neural network training.
Referring now to Figures 17 through 19, there are shown flowcharts illustrating the operation of the preferred embodiment of the transaction processing component. Some of the individual elements of the flowchart are indicated by de.sign,.tions which correspond to module names.
Referring now to Figure 17, there is shown the overall operation of transaction processing component 802. First the system runs module CINITNET 1702, which initi,.li7es network structures. Then, it runs module CSCORE 1703. Module CSCORE 1703 uses current trans-action data, data describing transactions over the past seven days, a profile record, and customer data to generate a fraud score indicating the likelihood that the current transaction is fr~lldl-lent, as well as reason codes (described below). The system then checks to see whether there are more transactions to be processed 1704, and repeats module CSCORE 1703 for any additional transactions. When there are no more to be processed, the system runs module FREENET 1705, which frees the network structures to allow them to be used for further procescing.
Referring now to Figure 18, there is shown the operation of module CSCORE 1703.
First, module CSCORE 1703 obtains current transaction data, data describing transactions of the past seven days, the profile record, and customer data (steps 1802 through 1805). From these data, module CSCORE 1703 generates the fraud-related variables 1806 described above. Then, it runs module DeployNet 1807, which applies the fraud-related variables to the stored neural network and provides a fraud score and reason codes. CSCORE then outputs the score and reason codes 1808.
Referring now to Figure 19, there is shown the operation of module DeployNet 1807.
Module DeployNet 1807 first scales the fraud-related variables 1902 to match the scaling previously performed in model development. If the value of a variable is mic.cing, DeployNet sets the value to equal the mean value found in the training set. Then it applies the scaled variables to the input layer of neural network 108, in step 1903. In step 1904, it processes the 0 6 ~ - 34 -applied data through the network to generate the fraud score. The method of iterating the network is well known in the art.
In addition to providing fraud scores, in step 1904, module DeployNet 1807 optionally generates "reason codes". These codes intlic~t~ which inputs to the model are most important in determining the fraud score for a given transaction. Any technique that can track such reasons may be used. In the preferred embodiment, the technique set forth in co-pending U.S. ap-plication Serial No. 07/814,179, (attorney's docket number 726) for "Neural Network Having Expert System Functionality", by Curt A. Levey, filed December 30, 1991, the disclosure of which is hereby incorporated by reference, is used.
The following module descriptions snmm~rize the functions performed by the individual modules.
FALCON C FILES
FILE NAME: CINITNET
DESCRIPTION: Contains code to allocate and illiti~lli7~ the network structures.
FUNCTION NAME: CINITNET() DESCRIPTION: Allocate and initi~li7e the network structures.
FILE NAME: CSCORE
DESCRIPTION: Generates fraud related variables and iterates the neural network.
FUNCTION NAME: SCORE() DESCRIPTION: Creates fraud related variables from raw variables and makes calls to initi~li7e the input layer and iterate the neural network.
FUNCTION NAME: setInput() DESCRIPTION: Sets the input value for a procec~ing element in the input layer.
FUNCTION NAME: hiReason() DESCRIPTION: Finds the three highest reasons for the score.
FILE NAME: CFREENET
DESCRIPTION: Makes function calls to free the network structures.
~14~68 WO 94/06103 ~ PCr/US93/08400 FUNCTION NAME: CFREENET() DESCRIPTION: Frees the network structures.
F~I,E NAME: CCREATEP
DESCRIPTION: Contains the cardholder profile creation code.
FUNCTION NAME: createpf() DESCRIPTION: Creates a profile record for a cardholder using the previous month's authorizations and cardholder data.
FILE NAME: CUPDATEP
DESCRIPTION: Updates a profile of individual cardholder activity.
FIJNCTION NAME: updatepf() DESCRIPTION: Updates a profile record for a cardholder using the previous profile record values as well as the previous month's authorizations and cardholder data.
FILE NAME: CCOMMON
DESCRIPTION: This file contains functions which are needed by at least two of the following:
createpf(), updatepf(), score().
FUNCTION NAME: accumMiscCnts() DESCRIPTION: Increments counters of various types for each authorization found.
FIJNCTION NAME: ~cum~icCnts() DESCRIPTION: Increments SIC variable counters.
FUNCTION NAME: initSicCounts() DESCRIPTION: Initializes the SIC variable counters.
FIJNCTION NAME: updateSicMovAvgs() DESCRIPTION: Updates the SIC profile variables.
FUNCTION NAME: writeMiscToProfile() DESCRIPI`ION: Writes various variables to the profile record after they have been calculated.
7 .' '~
2~44~ 36-FUNCTION NAME: hncDate() DESCRIPTION: Converts a Julian date to a date indicating the number of days since Jan. 1, 1990.
FUNCTION NAME: missStr() DESCRIPTION: Checks for "mi.ccin~" flag (a period) in a null terminated string.
String must have only blanks and a period to qualify as miccing A
string with only blanks will also qualify as "mic.cing'~
Cascaded Operation One way to improve system performance is via "c~cc~(le.d" operation. In cascadedoperation, more than one neural network model is used. The second neural network model is trained by model development component 801 in a similar manner to that described earlier.
However, in training the second model, model development component 801 uses only those transactions that have fraud scores, as det~rmin~ by prior application to the first neural network model, above a predetermined c~sc~cle threshold. Thus, the second model provides more accurate scores for high-scoring transactions. While the same fraud-related variables are available to train both models, it is often the case that different variables are found to be significant in the two models.
Referring now to Figure 20, there is shown a flowchart of the operation of the transaction procescing component in a cacc~clPcl system. First, transaction processing component 802 scores each transaction using the first model 2002, as described above. Those transactions that score above the c~sc~cle threshold 2003 are applied to the second neural network model 2005. The system outputs scores and reason codes from either the first model 2004 or the second model 2006, as a~lol ,iate.
The above-described c~cc~ling technique may be extended to include three or moreneural network models, each having a corresponding cascade threshold.
Performance Monitor The system periodically monitors its performance by measuring a performance metric comprising the fraud detection rate and the false positive rate. Other factors and statistics may also be incorporated into the performance metric. When the pelro"llance metric falls below a predetermined performance level, the system may either inform the user that the fraud model needs to be redeveloped, or it may proceed with model redevelopment autom~ti~lly.
From the above description, it will be apparent that the invention disclosed herein provides a novel and advantageous method of detecting fraudulent use of customer accounts and account numbers, which achieves high detection rates while keeping false positive rates relatively low. The foregoing discussion discloses and describes merely exemplary methods and O 94/06103 Zl 44 0 6~ Pcr/US93/08400 embodiments of the present invention. As will be understood by those f~mili~r with the art, the invention may be embodied in many other specific forms without departing from the spirit or es-sential characteristics thereof. For example, other predictive modeling techniques besides neural networks might be used. In addition, other variables might be used in both the model development and transaction procçs~ing components.
Accordingly, the disclosure of the present invention is inte~(lecl to be illustrative of the preferred embodiments and is not meant to limit the scope of the invention. The scope of the invention is to be limited only by the following claims.
Claims (31)
1. A computer-implemented process for detecting fraud for a transaction on a customer account, comprising the steps of:
developing a predictive model from past transaction data;
storing the predictive model in a medium associated with the computer;
obtaining current transaction data;
obtaining customer data;
generating a signal indicative of the likelihood of fraud responsive to application of the current transaction data and the customer data to the stored predictive model.
developing a predictive model from past transaction data;
storing the predictive model in a medium associated with the computer;
obtaining current transaction data;
obtaining customer data;
generating a signal indicative of the likelihood of fraud responsive to application of the current transaction data and the customer data to the stored predictive model.
2. The computer-implemented process of claim 1, wherein the step of obtaining customer data comprises accessing a database containing general customer data and a database containing customer transactional pattern data.
3. The computer-implemented process of claim 1, wherein the step of obtaining customer data comprises accessing no more than one profile database record containing customer transactional pattern data.
4. The computer-implemented process of claim 3, wherein the profile database record further contains general customer data.
5. The computer-implemented process of claim 1, wherein the current transaction data and the customer data each comprise a plurality of elements, the process further comprising the steps of, for each element of the current transaction data and the customer data:
determining a relative contribution of the element to the determined likelihood of fraud;
determining from each relative contribution thus determined a reason code value; and generating a signal indicative of the reason code value.
determining a relative contribution of the element to the determined likelihood of fraud;
determining from each relative contribution thus determined a reason code value; and generating a signal indicative of the reason code value.
6. The computer-implemented process of claim 1, further comprising the steps of:comparing the determined likelihood of fraud with a preset threshold value; and responsive to the likelihood of fraud exceeding the preset threshold value, signaling a fraud.
7. The computer-implemented process of claim 1, further comprising the iterative steps of:
comparing the determined likelihood of fraud with a cascade threshold value; andresponsive to the likelihood of fraud exceeding the cascade threshold value, generating another signal indicative of the likelihood of fraud responsive to application of the current transaction data and the customer data to another stored predictive model.
comparing the determined likelihood of fraud with a cascade threshold value; andresponsive to the likelihood of fraud exceeding the cascade threshold value, generating another signal indicative of the likelihood of fraud responsive to application of the current transaction data and the customer data to another stored predictive model.
8. The computer-implemented process of claim 1, further comprising the steps of:monitoring a performance metric of the predictive model;
comparing the performance metric with a predetermined performance level; and developing and storing a new predictive model from past transaction data responsive to the predetermined performance level exceeding the performance metric.
comparing the performance metric with a predetermined performance level; and developing and storing a new predictive model from past transaction data responsive to the predetermined performance level exceeding the performance metric.
9. The computer-implemented process of claim 8, wherein the performance metric comprises:
a fraud detection rate measurement; and a false positive rate measurement.
a fraud detection rate measurement; and a false positive rate measurement.
10. The computer-implemented process of claim 1, wherein the predictive model is a neural network.
11. The computer-implemented process of claim 1, wherein the step of developing the predictive model comprises the substeps of:
obtaining the past transaction data;
pre-processing the past transaction data to derive past fraud-related variables; and training the predictive model with the derived past fraud-related variables.
obtaining the past transaction data;
pre-processing the past transaction data to derive past fraud-related variables; and training the predictive model with the derived past fraud-related variables.
12. The computer-implemented process of claim 11, wherein the substep of training the predictive model comprises the iterative substeps of:
applying input data to the model;
ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the model responsive to the results of the ranking step.
applying input data to the model;
ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the model responsive to the results of the ranking step.
13. The computer-implemented process of claim 12, wherein the predictive model comprises a neural network having a plurality of interconnected processing elements, each processing element comprising:
a plurality of inputs;
a plurality of weights, each associated with a corresponding input to generate weighted inputs;
means for combining the weighted inputs; and a transfer function for processing the combined weighted inputs to produce an output.
a plurality of inputs;
a plurality of weights, each associated with a corresponding input to generate weighted inputs;
means for combining the weighted inputs; and a transfer function for processing the combined weighted inputs to produce an output.
14. The computer-implemented process of claim 13, wherein the substep of adjusting operation of the model comprises the substeps of:
selecting a subset of the weights to be decayed; and decaying the selected subset of weights.
selecting a subset of the weights to be decayed; and decaying the selected subset of weights.
15. The computer-implemented process of claim 14, wherein the substep of selecting a subset of the weights to be decayed comprises applying and minimizing a cost function including an interlayer gain multiplier which varies a decay rate responsive to the location of a weight within the network.
16. The computer-implemented process of claim 15, wherein the cost function is of the form:
, wherein:
D represents a data set;
targetk represents a target value for element k of the data set;
outputk represents a network output for element k of the data set;
g represents the interlayer gain multiplier;
l represents the relative importance of the complexity term;
W represents a weight set;
wi represents a value of weight i; and c1 represents a constant.
, wherein:
D represents a data set;
targetk represents a target value for element k of the data set;
outputk represents a network output for element k of the data set;
g represents the interlayer gain multiplier;
l represents the relative importance of the complexity term;
W represents a weight set;
wi represents a value of weight i; and c1 represents a constant.
17. A computer-implemented process for detecting fraud for a transaction on a customer account, comprising the steps of:
obtaining past transaction data;
pre-processing the past transaction data to derive past fraud-related variables;training a predictive model with the derived past fraud-related variables;
storing the predictive model in a medium associated with the computer;
obtaining current transaction data;
pre-processing the current transaction data to derive current fraud-related variables;
obtaining customer data;
pre-processing the customer data to derive customer fraud-related variables; andgenerating a signal responsive to the likelihood of fraud responsive to application of the current fraud-related variables and the customer fraud-related variables to the stored predictive model.
obtaining past transaction data;
pre-processing the past transaction data to derive past fraud-related variables;training a predictive model with the derived past fraud-related variables;
storing the predictive model in a medium associated with the computer;
obtaining current transaction data;
pre-processing the current transaction data to derive current fraud-related variables;
obtaining customer data;
pre-processing the customer data to derive customer fraud-related variables; andgenerating a signal responsive to the likelihood of fraud responsive to application of the current fraud-related variables and the customer fraud-related variables to the stored predictive model.
18. The computer-implemented process of claim 17, wherein the past fraud-relatedvariables and the current fraud-related variables each comprise at least:
factors obtained from data referring to transaction dollar amounts related to fraud;
factors obtained from data referring to transaction dates and times related to fraud;
factors obtained from data referring to transaction approvals and declines related to fraud;
and factors obtained from data referring to risk groups related to fraud.
factors obtained from data referring to transaction dollar amounts related to fraud;
factors obtained from data referring to transaction dates and times related to fraud;
factors obtained from data referring to transaction approvals and declines related to fraud;
and factors obtained from data referring to risk groups related to fraud.
19. The computer-implemented process of claim 17, wherein the past fraud-relatedvariables and the current fraud-related variables each comprise at least:
factors obtained from data referring to customers related to fraud; and factors obtained from data referring to merchants related to fraud.
factors obtained from data referring to customers related to fraud; and factors obtained from data referring to merchants related to fraud.
20. The computer-implemented process of claim 17, further comprising the steps of, for each of a set of the derived current fraud-related variables and the derived customer fraud-related variables:
determining a relative contribution of the variable to the determined likelihood of fraud to generate a reason code value;
determining from each relative contribution thus determined a reason code value; and generating a signal indicative of the reason code value.
determining a relative contribution of the variable to the determined likelihood of fraud to generate a reason code value;
determining from each relative contribution thus determined a reason code value; and generating a signal indicative of the reason code value.
21. The computer-implemented process of claim 17, wherein the predictive model is a neural network.
22. A computer-implemented process of training a predictive model, the predictive model for predicting outcomes based on selected characteristics, the predictive model stored in a medium associated with the computer, the process comprising the iterative steps of:
applying input data to the model;
ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the model responsive to the results of the ranking step.
applying input data to the model;
ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the model responsive to the results of the ranking step.
23. A computer-implemented process of training a neural network, the neural network for predicting outcomes based on selected characteristics, the neural network stored in a medium associated with the computer and comprising a plurality of interconnected processing elements, each processing element comprising:
a plurality of inputs;
a plurality of weights, each associated with a corresponding input to generate weighted inputs;
means for combining the weighted inputs; and a transfer function for processing the combined weighted inputs to produce an output;
the process comprising the iterative steps of:
applying input data to the neural network;
ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the neural network responsive to the results of the ranking step.
a plurality of inputs;
a plurality of weights, each associated with a corresponding input to generate weighted inputs;
means for combining the weighted inputs; and a transfer function for processing the combined weighted inputs to produce an output;
the process comprising the iterative steps of:
applying input data to the neural network;
ranking output data produced thereby responsive to a measure of quality; and adjusting operation of the neural network responsive to the results of the ranking step.
24. The computer-implemented process of claim 23, wherein the step of adjusting operation of the neural network comprises the substeps of:
selecting a subset of the weights to be decayed; and decaying the selected subset of weights.
selecting a subset of the weights to be decayed; and decaying the selected subset of weights.
25. The computer-implemented process of claim 24, wherein the substep of selecting a subset of the weights to be decayed comprises applying and minimizing a cost function including an interlayer gain multiplier which varies a decay rate responsive to the location of a weight within the network.
26. The computer-implemented process of claim 25, wherein the cost function is of the form:
, wherein:
D represents a data set;
targetk represents a target value for element k of the data set;
outputk represents a network output for element k of the data set;
g represents the interlayer gain multiplier;
l represents the relative importance of the complexity term;
W represents a weight set;
wi represents a value of weight i; and c1 represents a constant.
, wherein:
D represents a data set;
targetk represents a target value for element k of the data set;
outputk represents a network output for element k of the data set;
g represents the interlayer gain multiplier;
l represents the relative importance of the complexity term;
W represents a weight set;
wi represents a value of weight i; and c1 represents a constant.
27. A system for detecting fraud for a transaction on a customer account, comprising:
a predictive model for determining a likelihood of fraud for the transaction;
past transaction data input means for obtaining past transaction data;
a model development component, coupled to the predictive model, for training the prea predictive model for determining a likelihood of fraud for the transaction;
past transaction data input means for obtaining past transaction data;
a past transaction data pre-processor for deriving past fraud-related variables from the past transaction data;
a model development component, coupled to the predictive model, for training the pre-dictive model from the past fraud-related variables;
a storage device for storing the predictive model;
current transaction data input means for obtaining current transaction data for the transaction;
a current transaction data pre-processor for deriving current fraud-related variables from the current transaction data and sending the current fraud-related variables to the predictive model;
customer data input means for obtaining customer data;
a customer data pre-processor for deriving customer fraud-related variables from the customer data and sending the customer fraud-related variables to the predictivedictive model from the past transaction data;
a storage device for storing the trained predictive model;
current transaction data input means for obtaining current transaction data and sending the current transaction data to the predictive model;
customer data input means for obtaining and sending to the predictive model, customer data; and an output device, coupled to the predictive model, for generating a signal responsive to the likelihood of fraud.
a predictive model for determining a likelihood of fraud for the transaction;
past transaction data input means for obtaining past transaction data;
a model development component, coupled to the predictive model, for training the prea predictive model for determining a likelihood of fraud for the transaction;
past transaction data input means for obtaining past transaction data;
a past transaction data pre-processor for deriving past fraud-related variables from the past transaction data;
a model development component, coupled to the predictive model, for training the pre-dictive model from the past fraud-related variables;
a storage device for storing the predictive model;
current transaction data input means for obtaining current transaction data for the transaction;
a current transaction data pre-processor for deriving current fraud-related variables from the current transaction data and sending the current fraud-related variables to the predictive model;
customer data input means for obtaining customer data;
a customer data pre-processor for deriving customer fraud-related variables from the customer data and sending the customer fraud-related variables to the predictivedictive model from the past transaction data;
a storage device for storing the trained predictive model;
current transaction data input means for obtaining current transaction data and sending the current transaction data to the predictive model;
customer data input means for obtaining and sending to the predictive model, customer data; and an output device, coupled to the predictive model, for generating a signal responsive to the likelihood of fraud.
28. The system of claim 27, wherein the model development component comprises a past transaction data pre-processor for deriving past fraud-related variables from the past transaction data.
29. The system of claim 27, wherein the predictive model comprises a neural network.
30. A system for detecting fraud for a transaction on an account belonging to a customer, comprising:
model; and an output device, coupled to the predictive model, for generating a signal responsive to the likelihood of fraud.
model; and an output device, coupled to the predictive model, for generating a signal responsive to the likelihood of fraud.
31. The system of claim 29, wherein the predictive model comprises a neural network.
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US07/941,971 | 1992-09-08 | ||
US07/941,971 US5819226A (en) | 1992-09-08 | 1992-09-08 | Fraud detection using predictive modeling |
Publications (1)
Publication Number | Publication Date |
---|---|
CA2144068A1 true CA2144068A1 (en) | 1994-03-17 |
Family
ID=25477381
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CA002144068A Abandoned CA2144068A1 (en) | 1992-09-08 | 1993-09-07 | Fraud detection using predictive modeling |
Country Status (8)
Country | Link |
---|---|
US (2) | US5819226A (en) |
EP (1) | EP0669032B1 (en) |
JP (1) | JPH08504284A (en) |
AU (1) | AU4850093A (en) |
CA (1) | CA2144068A1 (en) |
DE (1) | DE69315356T2 (en) |
ES (1) | ES2108880T3 (en) |
WO (1) | WO1994006103A1 (en) |
Families Citing this family (894)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6185415B1 (en) * | 1992-03-24 | 2001-02-06 | Atcomm Corporation | Call security system |
US7747243B2 (en) * | 1992-03-24 | 2010-06-29 | Boatwright John T | Call security system |
US5819226A (en) * | 1992-09-08 | 1998-10-06 | Hnc Software Inc. | Fraud detection using predictive modeling |
US7251624B1 (en) | 1992-09-08 | 2007-07-31 | Fair Isaac Corporation | Score based decisioning |
US6658568B1 (en) | 1995-02-13 | 2003-12-02 | Intertrust Technologies Corporation | Trusted infrastructure support system, methods and techniques for secure electronic commerce transaction and rights management |
US6157721A (en) | 1996-08-12 | 2000-12-05 | Intertrust Technologies Corp. | Systems and methods using cryptography to protect secure computing environments |
US20120166807A1 (en) | 1996-08-12 | 2012-06-28 | Intertrust Technologies Corp. | Systems and Methods Using Cryptography to Protect Secure Computing Environments |
US5892900A (en) | 1996-08-30 | 1999-04-06 | Intertrust Technologies Corp. | Systems and methods for secure transaction management and electronic rights protection |
EP1555591B1 (en) * | 1995-02-13 | 2013-08-14 | Intertrust Technologies Corp. | Secure transaction management |
FR2732486B1 (en) * | 1995-03-31 | 1997-05-09 | Solaic Sa | METHOD FOR RELIABILITY OF A REQUEST FOR ACCESS TO THE APPLICATION MANAGEMENT PROGRAM OF A MEMORY CARD, AND MEMORY CARD FOR THE IMPLEMENTATION OF THIS METHOD |
GB2303275B (en) * | 1995-07-13 | 1997-06-25 | Northern Telecom Ltd | Detecting mobile telephone misuse |
US20010011253A1 (en) | 1998-08-04 | 2001-08-02 | Christopher D. Coley | Automated system for management of licensed software |
DE19611732C1 (en) * | 1996-03-25 | 1997-04-30 | Siemens Ag | Neural network weightings suitable for removal or pruning determination method |
US8229844B2 (en) | 1996-06-05 | 2012-07-24 | Fraud Control Systems.Com Corporation | Method of billing a purchase made over a computer network |
US7555458B1 (en) | 1996-06-05 | 2009-06-30 | Fraud Control System.Com Corporation | Method of billing a purchase made over a computer network |
US20030195846A1 (en) | 1996-06-05 | 2003-10-16 | David Felger | Method of billing a purchase made over a computer network |
US6094643A (en) * | 1996-06-14 | 2000-07-25 | Card Alert Services, Inc. | System for detecting counterfeit financial card fraud |
US7590853B1 (en) | 1996-08-12 | 2009-09-15 | Intertrust Technologies Corporation | Systems and methods using cryptography to protect secure computing environments |
US6253186B1 (en) * | 1996-08-14 | 2001-06-26 | Blue Cross Blue Shield Of South Carolina | Method and apparatus for detecting fraud |
US6430305B1 (en) * | 1996-12-20 | 2002-08-06 | Synaptics, Incorporated | Identity verification methods |
GB2321364A (en) * | 1997-01-21 | 1998-07-22 | Northern Telecom Ltd | Retraining neural network |
GB2321362A (en) * | 1997-01-21 | 1998-07-22 | Northern Telecom Ltd | Generic processing capability |
GB2321363A (en) | 1997-01-21 | 1998-07-22 | Northern Telecom Ltd | Telecommunications |
US6119103A (en) * | 1997-05-27 | 2000-09-12 | Visa International Service Association | Financial risk prediction systems and methods therefor |
US6018723A (en) | 1997-05-27 | 2000-01-25 | Visa International Service Association | Method and apparatus for pattern generation |
US5949045A (en) * | 1997-07-03 | 1999-09-07 | At&T Corp. | Micro-dynamic simulation of electronic cash transactions |
DE19729631A1 (en) * | 1997-07-10 | 1999-01-14 | Siemens Ag | Detection of a fraudulent call using a neural network |
DE19729630A1 (en) * | 1997-07-10 | 1999-01-14 | Siemens Ag | Detection of a fraudulent call using a neural network |
US7096192B1 (en) | 1997-07-28 | 2006-08-22 | Cybersource Corporation | Method and system for detecting fraud in a credit card transaction over a computer network |
US7403922B1 (en) * | 1997-07-28 | 2008-07-22 | Cybersource Corporation | Method and apparatus for evaluating fraud risk in an electronic commerce transaction |
US6029154A (en) * | 1997-07-28 | 2000-02-22 | Internet Commerce Services Corporation | Method and system for detecting fraud in a credit card transaction over the internet |
US7263527B1 (en) * | 1997-08-11 | 2007-08-28 | International Business Machines Corporation | Grouping selected transactions in account ledger |
US6064972A (en) * | 1997-09-17 | 2000-05-16 | At&T Corp | Risk management technique for network access |
US6349288B1 (en) * | 1997-11-18 | 2002-02-19 | Timothy P. Barber | Architecture for access over a network to pay-per-view information |
WO1999027466A2 (en) * | 1997-11-26 | 1999-06-03 | The Government Of The United States Of America, As Represented By The Secretary, Department Of Health And Human Services, The National Institutes Of Health | System and method for intelligent quality control of a process |
US6430615B1 (en) * | 1998-03-13 | 2002-08-06 | International Business Machines Corporation | Predictive model-based measurement acquisition employing a predictive model operating on a manager system and a managed system |
US6163604A (en) * | 1998-04-03 | 2000-12-19 | Lucent Technologies | Automated fraud management in transaction-based networks |
US6157707A (en) * | 1998-04-03 | 2000-12-05 | Lucent Technologies Inc. | Automated and selective intervention in transaction-based networks |
US6510419B1 (en) * | 1998-04-24 | 2003-01-21 | Starmine Corporation | Security analyst performance tracking and analysis system and method |
US7603308B2 (en) | 1998-04-24 | 2009-10-13 | Starmine Corporation | Security analyst estimates performance viewing system and method |
US7167838B1 (en) | 1998-04-24 | 2007-01-23 | Starmine Corporation | Security analyst estimates performance viewing system and method |
US7509277B1 (en) | 1998-04-24 | 2009-03-24 | Starmine Corporation | Security analyst estimates performance viewing system and method |
US7539637B2 (en) | 1998-04-24 | 2009-05-26 | Starmine Corporation | Security analyst estimates performance viewing system and method |
US6263447B1 (en) | 1998-05-21 | 2001-07-17 | Equifax Inc. | System and method for authentication of network users |
US6615189B1 (en) | 1998-06-22 | 2003-09-02 | Bank One, Delaware, National Association | Debit purchasing of stored value card for use by and/or delivery to others |
US7809642B1 (en) | 1998-06-22 | 2010-10-05 | Jpmorgan Chase Bank, N.A. | Debit purchasing of stored value card for use by and/or delivery to others |
US6115709A (en) * | 1998-09-18 | 2000-09-05 | Tacit Knowledge Systems, Inc. | Method and system for constructing a knowledge profile of a user having unrestricted and restricted access portions according to respective levels of confidence of content of the portions |
US7337119B1 (en) | 1998-10-26 | 2008-02-26 | First Data Corporation | System and method for detecting purchasing card fraud |
US8010422B1 (en) | 1998-11-03 | 2011-08-30 | Nextcard, Llc | On-line balance transfers |
US6567791B2 (en) * | 1998-11-03 | 2003-05-20 | Nextcard, Inc. | Method and apparatus for a verifiable on line rejection of an application for credit |
US6032136A (en) | 1998-11-17 | 2000-02-29 | First Usa Bank, N.A. | Customer activated multi-value (CAM) card |
US7660763B1 (en) | 1998-11-17 | 2010-02-09 | Jpmorgan Chase Bank, N.A. | Customer activated multi-value (CAM) card |
EP1131976A1 (en) * | 1998-11-18 | 2001-09-12 | Lightbridge, Inc. | Event manager for use in fraud detection |
US7058597B1 (en) * | 1998-12-04 | 2006-06-06 | Digital River, Inc. | Apparatus and method for adaptive fraud screening for electronic commerce transactions |
US7617124B1 (en) | 1998-12-04 | 2009-11-10 | Digital River, Inc. | Apparatus and method for secure downloading of files |
US20030195974A1 (en) * | 1998-12-04 | 2003-10-16 | Ronning Joel A. | Apparatus and method for scheduling of search for updates or downloads of a file |
US7035855B1 (en) * | 2000-07-06 | 2006-04-25 | Experian Marketing Solutions, Inc. | Process and system for integrating information from disparate databases for purposes of predicting consumer behavior |
US6526389B1 (en) * | 1999-04-20 | 2003-02-25 | Amdocs Software Systems Limited | Telecommunications system for generating a three-level customer behavior profile and for detecting deviation from the profile to identify fraud |
GB9910111D0 (en) * | 1999-04-30 | 1999-06-30 | Nortel Networks Corp | Account fraud scoring |
US6430539B1 (en) | 1999-05-06 | 2002-08-06 | Hnc Software | Predictive modeling of consumer financial behavior |
US7165045B1 (en) * | 1999-05-19 | 2007-01-16 | Miral Kim-E | Network-based trading system and method |
US6904409B1 (en) * | 1999-06-01 | 2005-06-07 | Lucent Technologies Inc. | Method for constructing an updateable database of subject behavior patterns |
US7013296B1 (en) | 1999-06-08 | 2006-03-14 | The Trustees Of Columbia University In The City Of New York | Using electronic security value units to control access to a resource |
US7140039B1 (en) | 1999-06-08 | 2006-11-21 | The Trustees Of Columbia University In The City Of New York | Identification of an attacker in an electronic system |
US6871194B1 (en) * | 1999-07-13 | 2005-03-22 | Compudigm International Limited | Interaction prediction system and method |
US8666757B2 (en) * | 1999-07-28 | 2014-03-04 | Fair Isaac Corporation | Detection of upcoding and code gaming fraud and abuse in prospective payment healthcare systems |
WO2001009746A1 (en) * | 1999-07-28 | 2001-02-08 | Hnc Software, Inc. | Cascaded profiles for multiple interacting entities |
US7379880B1 (en) * | 1999-07-28 | 2008-05-27 | Fair Isaac Corporation | Cascaded profiles for multiple interacting entities |
US7243236B1 (en) | 1999-07-29 | 2007-07-10 | Intertrust Technologies Corp. | Systems and methods for using cryptography to protect secure and insecure computing environments |
DE60023013T2 (en) | 1999-07-30 | 2006-06-29 | Intertrust Technologies Corp., Santa Clara | METHOD AND SYSTEMS FOR THE TRANSACTION RECORD TRANSMISSION USING THRESHOLD AND A MULTI-STAGE PROTOCOL |
US8266025B1 (en) * | 1999-08-09 | 2012-09-11 | Citibank, N.A. | System and method for assuring the integrity of data used to evaluate financial risk or exposure |
US7813944B1 (en) * | 1999-08-12 | 2010-10-12 | Fair Isaac Corporation | Detection of insurance premium fraud or abuse using a predictive software system |
US7406603B1 (en) | 1999-08-31 | 2008-07-29 | Intertrust Technologies Corp. | Data protection systems and methods |
EP1081665B1 (en) | 1999-09-02 | 2005-06-08 | GZS Gesellschaft für Zahlungssysteme mbH | Expert system |
WO2001018755A2 (en) * | 1999-09-02 | 2001-03-15 | Gzs Gesellschaft Für Zahlungssysteme Mbh | Expert system |
US7239226B2 (en) | 2001-07-10 | 2007-07-03 | American Express Travel Related Services Company, Inc. | System and method for payment using radio frequency identification in contact and contactless transactions |
US6985885B1 (en) | 1999-09-21 | 2006-01-10 | Intertrust Technologies Corp. | Systems and methods for pricing and selling digital goods |
GB2354609B (en) * | 1999-09-25 | 2003-07-16 | Ibm | Method and system for predicting transactions |
US7418431B1 (en) * | 1999-09-30 | 2008-08-26 | Fair Isaac Corporation | Webstation: configurable web-based workstation for reason driven data analysis |
US8103585B2 (en) * | 1999-11-05 | 2012-01-24 | American Express Travel Related Services Company, Inc. | Systems and methods for suggesting an allocation |
US7904385B2 (en) * | 1999-11-05 | 2011-03-08 | American Express Travel Related Services Company, Inc. | Systems and methods for facilitating budgeting transactions |
US8103584B2 (en) * | 1999-11-05 | 2012-01-24 | American Express Travel Related Services Company, Inc. | Systems and methods for authorizing an allocation of an amount between transaction accounts |
AU1757201A (en) * | 1999-11-05 | 2001-05-14 | American Express Travel Related Services Company, Inc. | Systems and methods for facilitating commercial transactions between parties residing at remote locations |
US20090265250A1 (en) * | 1999-11-05 | 2009-10-22 | American Express Travel Related Services Company, Inc. | Systems and methods for processing a transaction according to an allowance |
US8875990B2 (en) * | 1999-11-05 | 2014-11-04 | Lead Core Fund, L.L.C. | Systems and methods for allocating a payment authorization request to a payment processor |
US8814039B2 (en) * | 1999-11-05 | 2014-08-26 | Lead Core Fund, L.L.C. | Methods for processing a payment authorization request utilizing a network of point of sale devices |
US7925585B2 (en) * | 1999-11-05 | 2011-04-12 | American Express Travel Related Services Company, Inc. | Systems and methods for facilitating transactions with different account issuers |
US8646685B2 (en) * | 1999-11-05 | 2014-02-11 | Lead Core Fund, L.L.C. | Device for allocating a payment authorization request to a payment processor |
US8851369B2 (en) * | 1999-11-05 | 2014-10-07 | Lead Core Fund, L.L.C. | Systems and methods for transaction processing using a smartcard |
US20090164329A1 (en) * | 1999-11-05 | 2009-06-25 | American Express Travel Related Services Company, Inc. | Systems for Processing a Payment Authorization Request Utilizing a Network of Point of Sale Devices |
US20090048886A1 (en) * | 1999-11-05 | 2009-02-19 | American Express Travel Related Services Company, Inc. | Systems and Methods for Facilitating Gifting Transactions |
US7908214B2 (en) * | 1999-11-05 | 2011-03-15 | American Express Travel Related Services Company, Inc. | Systems and methods for adjusting loan amounts to facilitate transactions |
US8794509B2 (en) * | 1999-11-05 | 2014-08-05 | Lead Core Fund, L.L.C. | Systems and methods for processing a payment authorization request over disparate payment networks |
US8820633B2 (en) * | 1999-11-05 | 2014-09-02 | Lead Core Fund, L.L.C. | Methods for a third party biller to receive an allocated payment authorization request |
US20090164331A1 (en) * | 1999-11-05 | 2009-06-25 | American Express Travel Related Services Company, Inc. | Systems for Locating a Payment System Utilizing a Point of Sale Device |
US8458086B2 (en) * | 1999-11-05 | 2013-06-04 | Lead Core Fund, L.L.C. | Allocating partial payment of a transaction amount using an allocation rule |
US7962406B2 (en) * | 1999-11-05 | 2011-06-14 | American Express Travel Related Services Company, Inc. | Systems and methods for facilitating transactions |
US8073772B2 (en) * | 1999-11-05 | 2011-12-06 | American Express Travel Related Services Company, Inc. | Systems and methods for processing transactions using multiple budgets |
US20090048885A1 (en) * | 1999-11-05 | 2009-02-19 | American Express Travel Related Services Company, Inc. | Systems and Methods for Facilitating Cost-Splitting Transactions |
US7996307B2 (en) * | 1999-11-05 | 2011-08-09 | American Express Travel Related Services Company, Inc. | Systems and methods for facilitating transactions between different financial accounts |
US7962408B2 (en) * | 1999-11-05 | 2011-06-14 | American Express Travel Related Services Company, Inc. | Systems and methods for establishing an allocation of an amount between transaction accounts |
US8234212B2 (en) * | 1999-11-05 | 2012-07-31 | Lead Core Fund, L.L.C. | Systems and methods for facilitating transactions with interest |
US8190514B2 (en) * | 1999-11-05 | 2012-05-29 | Lead Core Fund, L.L.C. | Systems and methods for transaction processing based upon an overdraft scenario |
US20090265249A1 (en) * | 1999-11-05 | 2009-10-22 | American Express Travel Related Services Company, Inc. | Systems and methods for split tender transaction processing |
US8596527B2 (en) * | 1999-11-05 | 2013-12-03 | Lead Core Fund, L.L.C. | Methods for locating a payment system utilizing a point of sale device |
US8180706B2 (en) * | 1999-11-05 | 2012-05-15 | Lead Core Fund, L.L.C. | Systems and methods for maximizing a rewards accumulation strategy during transaction processing |
US7941372B2 (en) * | 1999-11-05 | 2011-05-10 | American Express Travel Related Services Company, Inc. | Systems and methods for receiving an allocation of an amount between transaction accounts |
US7877325B2 (en) * | 1999-11-05 | 2011-01-25 | American Express Travel Related Services Company, Inc. | Systems and methods for settling an allocation of an amount between transaction accounts |
US7899744B2 (en) * | 1999-11-05 | 2011-03-01 | American Express Travel Related Services Company, Inc. | Systems and methods for approval of an allocation |
US8195565B2 (en) * | 1999-11-05 | 2012-06-05 | Lead Core Fund, L.L.C. | Systems and methods for point of interaction based policy routing of transactions |
US7979349B2 (en) * | 1999-11-05 | 2011-07-12 | American Express Travel Related Services Company, Inc. | Systems and methods for adjusting crediting limits to facilitate transactions |
US20090048887A1 (en) * | 1999-11-05 | 2009-02-19 | American Express Travel Related Services Company, Inc. | Systems and Methods for Facilitating Transactions Involving an Intermediary |
US8275704B2 (en) * | 1999-11-05 | 2012-09-25 | Lead Core Fund, L.L.C. | Systems and methods for authorizing an allocation of an amount between transaction accounts |
US20090265241A1 (en) * | 1999-11-05 | 2009-10-22 | American Express Travel Related Services Company, Inc. | Systems and methods for determining a rewards account to fund a transaction |
US20090164325A1 (en) * | 1999-11-05 | 2009-06-25 | American Express Travel Related Services Company, Inc. | Systems and Methods for Locating an Automated Clearing House Utilizing a Point of Sale Device |
US7941367B2 (en) * | 1999-11-05 | 2011-05-10 | American Express Travel Related Services Company, Inc. | Systems and methods for allocating an amount between sub-accounts |
US7962407B2 (en) * | 1999-11-05 | 2011-06-14 | American Express Travel Related Services Company, Inc. | Systems and methods for allocating an amount between transaction accounts |
US20090164328A1 (en) * | 1999-11-05 | 2009-06-25 | American Express Travel Related Services Company, Inc. | Systems and Methods for Locating a Payment System and Determining a Taxing Authority Utilizing a Point of Sale Device |
US6876991B1 (en) | 1999-11-08 | 2005-04-05 | Collaborative Decision Platforms, Llc. | System, method and computer program product for a collaborative decision platform |
US6601014B1 (en) * | 1999-11-30 | 2003-07-29 | Cerebrus Solutions Ltd. | Dynamic deviation |
US8793160B2 (en) | 1999-12-07 | 2014-07-29 | Steve Sorem | System and method for processing transactions |
JP2001175606A (en) * | 1999-12-20 | 2001-06-29 | Sony Corp | Data processor, and data processing equipment and its method |
US6985901B1 (en) | 1999-12-23 | 2006-01-10 | Accenture Llp | Controlling data collection, manipulation and storage on a network with service assurance capabilities |
US8036978B1 (en) * | 1999-12-31 | 2011-10-11 | Pitney Bowes Inc. | Method of upgrading third party functionality in an electronic fraud management system |
US6779120B1 (en) * | 2000-01-07 | 2004-08-17 | Securify, Inc. | Declarative language for specifying a security policy |
US8074256B2 (en) * | 2000-01-07 | 2011-12-06 | Mcafee, Inc. | Pdstudio design system and method |
US20030018550A1 (en) * | 2000-02-22 | 2003-01-23 | Rotman Frank Lewis | Methods and systems for providing transaction data |
US6999943B1 (en) | 2000-03-10 | 2006-02-14 | Doublecredit.Com, Inc. | Routing methods and systems for increasing payment transaction volume and profitability |
WO2001073652A1 (en) * | 2000-03-24 | 2001-10-04 | Access Business Group International Llc | System and method for detecting fraudulent transactions |
US7174318B2 (en) | 2000-03-28 | 2007-02-06 | Richard Adelson | Method and system for an online-like account processing and management |
US7263506B2 (en) * | 2000-04-06 | 2007-08-28 | Fair Isaac Corporation | Identification and management of fraudulent credit/debit card purchases at merchant ecommerce sites |
US8775284B1 (en) * | 2000-04-07 | 2014-07-08 | Vectorsgi, Inc. | System and method for evaluating fraud suspects |
US7113914B1 (en) * | 2000-04-07 | 2006-09-26 | Jpmorgan Chase Bank, N.A. | Method and system for managing risks |
US7562042B2 (en) * | 2000-04-07 | 2009-07-14 | Massachusetts Institute Of Technology | Data processor for implementing forecasting algorithms |
US8046288B1 (en) * | 2000-06-13 | 2011-10-25 | Paymentech, Llc | System and method for payment data processing |
US7917647B2 (en) | 2000-06-16 | 2011-03-29 | Mcafee, Inc. | Method and apparatus for rate limiting |
US20030208689A1 (en) * | 2000-06-16 | 2003-11-06 | Garza Joel De La | Remote computer forensic evidence collection system and process |
US7376618B1 (en) * | 2000-06-30 | 2008-05-20 | Fair Isaac Corporation | Detecting and measuring risk with predictive models using content mining |
US7610216B1 (en) * | 2000-07-13 | 2009-10-27 | Ebay Inc. | Method and system for detecting fraud |
WO2002021313A2 (en) * | 2000-09-05 | 2002-03-14 | Bloodhound Software, Inc. | Unsupervised method of identifying aberrant behavior by an entity with respect to healthcare claim transactions and associated computer software program product, computer device, and system |
FI114749B (en) * | 2000-09-11 | 2004-12-15 | Nokia Corp | Anomaly detection system and method for teaching it |
US20020082977A1 (en) * | 2000-09-25 | 2002-06-27 | Hammond Mark S. | Aggregation of on-line auction listing and market data for use to increase likely revenues from auction listings |
WO2002027610A1 (en) * | 2000-09-29 | 2002-04-04 | Hnc Software, Inc. | Score based decisioning |
JP2002123685A (en) * | 2000-10-13 | 2002-04-26 | Mitsubishi Electric Corp | Information terminal |
US6904408B1 (en) * | 2000-10-19 | 2005-06-07 | Mccarthy John | Bionet method, system and personalized web content manager responsive to browser viewers' psychological preferences, behavioral responses and physiological stress indicators |
US9819561B2 (en) | 2000-10-26 | 2017-11-14 | Liveperson, Inc. | System and methods for facilitating object assignments |
US8868448B2 (en) | 2000-10-26 | 2014-10-21 | Liveperson, Inc. | Systems and methods to facilitate selling of products and services |
US7333953B1 (en) | 2000-10-31 | 2008-02-19 | Wells Fargo Bank, N.A. | Method and apparatus for integrated payments processing and decisioning for internet transactions |
US8145567B2 (en) | 2000-10-31 | 2012-03-27 | Wells Fargo Bank, N.A. | Transaction ID system and process |
US7313544B1 (en) * | 2000-11-01 | 2007-12-25 | Capital One Financial Corporation | System and method for restricting over-limit accounts |
CA2426168A1 (en) * | 2000-11-02 | 2002-05-10 | Cybersource Corporation | Method and apparatus for evaluating fraud risk in an electronic commerce transaction |
US7415432B1 (en) * | 2000-11-17 | 2008-08-19 | D.E. Shaw & Co., Inc. | Method and apparatus for the receipt, combination, and evaluation of equity portfolios for execution by a sponsor at passively determined prices |
IL146597A0 (en) * | 2001-11-20 | 2002-08-14 | Gordon Goren | Method and system for creating meaningful summaries from interrelated sets of information |
GB0029229D0 (en) * | 2000-11-30 | 2001-01-17 | Unisys Corp | Counter measures for irregularities in financial transactions |
US7433829B2 (en) * | 2000-12-12 | 2008-10-07 | Jpmorgan Chase Bank, N.A. | System and method for managing global risk |
US6636850B2 (en) | 2000-12-28 | 2003-10-21 | Fairisaac And Company, Inc. | Aggregate score matching system for transaction records |
US20020087460A1 (en) * | 2001-01-04 | 2002-07-04 | Hornung Katharine A. | Method for identity theft protection |
FR2819322A1 (en) * | 2001-01-08 | 2002-07-12 | Verisec | Method for assessing and managing security of computer system of certain configuration by performing comparative analysis between first and second databases to assess possible problems that might affect computer system |
US7065644B2 (en) * | 2001-01-12 | 2006-06-20 | Hewlett-Packard Development Company, L.P. | System and method for protecting a security profile of a computer system |
US20020147754A1 (en) * | 2001-01-31 | 2002-10-10 | Dempsey Derek M. | Vector difference measures for data classifiers |
US20020147694A1 (en) * | 2001-01-31 | 2002-10-10 | Dempsey Derek M. | Retraining trainable data classifiers |
CA2354372A1 (en) * | 2001-02-23 | 2002-08-23 | Efunds Corporation | Electronic payment and authentication system with debit and identification data verification and electronic check capabilities |
US7249094B2 (en) * | 2001-02-26 | 2007-07-24 | Paypal, Inc. | System and method for depicting on-line transactions |
WO2002069561A2 (en) * | 2001-02-27 | 2002-09-06 | Visa International Service Association | Distributed quantum encrypted pattern generation and scoring |
US7809650B2 (en) * | 2003-07-01 | 2010-10-05 | Visa U.S.A. Inc. | Method and system for providing risk information in connection with transaction processing |
DE60236351D1 (en) * | 2001-03-08 | 2010-06-24 | California Inst Of Techn | REAL-TIME REAL-TIME COHERENCE ASSESSMENT FOR AUTONOMOUS MODUS IDENTIFICATION AND INVARIATION TRACKING |
US7552080B1 (en) | 2001-03-09 | 2009-06-23 | Nextcard, Llc | Customized credit offer strategy based on terms specified by an applicant |
US6783065B2 (en) | 2001-03-12 | 2004-08-31 | First Data Corporation | Purchasing card transaction risk model |
US7089592B2 (en) * | 2001-03-15 | 2006-08-08 | Brighterion, Inc. | Systems and methods for dynamic detection and prevention of electronic fraud |
US20020138417A1 (en) * | 2001-03-20 | 2002-09-26 | David Lawrence | Risk management clearinghouse |
US7904361B2 (en) | 2001-03-20 | 2011-03-08 | Goldman Sachs & Co. | Risk management customer registry |
US7899722B1 (en) | 2001-03-20 | 2011-03-01 | Goldman Sachs & Co. | Correspondent bank registry |
US8209246B2 (en) | 2001-03-20 | 2012-06-26 | Goldman, Sachs & Co. | Proprietary risk management clearinghouse |
US8140415B2 (en) | 2001-03-20 | 2012-03-20 | Goldman Sachs & Co. | Automated global risk management |
US8527400B2 (en) * | 2001-03-20 | 2013-09-03 | Goldman, Sachs & Co. | Automated account risk management |
US8285615B2 (en) * | 2001-03-20 | 2012-10-09 | Goldman, Sachs & Co. | Construction industry risk management clearinghouse |
US8069105B2 (en) | 2001-03-20 | 2011-11-29 | Goldman Sachs & Co. | Hedge fund risk management |
US7548883B2 (en) * | 2001-03-20 | 2009-06-16 | Goldman Sachs & Co | Construction industry risk management clearinghouse |
US8121937B2 (en) | 2001-03-20 | 2012-02-21 | Goldman Sachs & Co. | Gaming industry risk management clearinghouse |
US7958027B2 (en) | 2001-03-20 | 2011-06-07 | Goldman, Sachs & Co. | Systems and methods for managing risk associated with a geo-political area |
US20020161711A1 (en) * | 2001-04-30 | 2002-10-31 | Sartor Karalyn K. | Fraud detection method |
US7590594B2 (en) * | 2001-04-30 | 2009-09-15 | Goldman Sachs & Co. | Method, software program, and system for ranking relative risk of a plurality of transactions |
US6907426B2 (en) * | 2001-05-17 | 2005-06-14 | International Business Machines Corporation | Systems and methods for identifying and counting instances of temporal patterns |
US7313546B2 (en) | 2001-05-23 | 2007-12-25 | Jp Morgan Chase Bank, N.A. | System and method for currency selectable stored value instrument |
US7865427B2 (en) | 2001-05-30 | 2011-01-04 | Cybersource Corporation | Method and apparatus for evaluating fraud risk in an electronic commerce transaction |
WO2003001866A1 (en) | 2001-06-27 | 2003-01-09 | Snapcount Limited | Transcation processing |
US7383224B2 (en) * | 2001-06-29 | 2008-06-03 | Capital One Financial Corporation | Systems and methods for processing credit card transactions that exceed a credit limit |
US7493288B2 (en) | 2001-07-10 | 2009-02-17 | Xatra Fund Mx, Llc | RF payment via a mobile device |
US7249112B2 (en) | 2002-07-09 | 2007-07-24 | American Express Travel Related Services Company, Inc. | System and method for assigning a funding source for a radio frequency identification device |
US7983879B1 (en) | 2001-07-20 | 2011-07-19 | The Mathworks, Inc. | Code generation for data acquisition and/or logging in a modeling environment |
US7613716B2 (en) | 2001-07-20 | 2009-11-03 | The Mathworks, Inc. | Partitioning for model-based design |
WO2003010701A1 (en) | 2001-07-24 | 2003-02-06 | First Usa Bank, N.A. | Multiple account card and transaction routing |
US7835919B1 (en) * | 2001-08-10 | 2010-11-16 | Freddie Mac | Systems and methods for home value scoring |
US7711574B1 (en) | 2001-08-10 | 2010-05-04 | Federal Home Loan Mortgage Corporation (Freddie Mac) | System and method for providing automated value estimates of properties as of a specified previous time period |
US8020754B2 (en) | 2001-08-13 | 2011-09-20 | Jpmorgan Chase Bank, N.A. | System and method for funding a collective account by use of an electronic tag |
US7311244B1 (en) | 2001-08-13 | 2007-12-25 | Jpmorgan Chase Bank, N.A. | System and method for funding a collective account by use of an electronic tag |
GB2379045A (en) * | 2001-08-24 | 2003-02-26 | Hewlett Packard Co | Account controller |
US7313545B2 (en) * | 2001-09-07 | 2007-12-25 | First Data Corporation | System and method for detecting fraudulent calls |
US7386510B2 (en) * | 2001-09-07 | 2008-06-10 | First Data Corporation | System and method for detecting fraudulent calls |
US7636680B2 (en) | 2001-10-03 | 2009-12-22 | Starmine Corporation | Methods and systems for measuring performance of a security analyst |
US6975996B2 (en) * | 2001-10-09 | 2005-12-13 | Goldman, Sachs & Co. | Electronic subpoena service |
US20030074308A1 (en) * | 2001-10-12 | 2003-04-17 | Lawton Brian Michael | System and method for optimizing collection from skip accounts |
EP1436746A4 (en) * | 2001-10-17 | 2007-10-10 | Npx Technologies Ltd | Verification of a person identifier received online |
AUPR863001A0 (en) * | 2001-11-01 | 2001-11-29 | Inovatech Limited | Wavelet based fraud detection |
US8458082B2 (en) | 2001-11-13 | 2013-06-04 | Interthinx, Inc. | Automated loan risk assessment system and method |
US7689503B2 (en) * | 2001-11-13 | 2010-03-30 | Interthinx, Inc. | Predatory lending detection system and method therefor |
AU2002367595A1 (en) * | 2001-11-28 | 2003-09-22 | Goldman, Sachs And Co. | Transaction surveillance |
US20040030644A1 (en) * | 2001-12-14 | 2004-02-12 | Shaper Stephen J. | Systems for facilitating card processing systems/improved risk control |
JP4082028B2 (en) * | 2001-12-28 | 2008-04-30 | ソニー株式会社 | Information processing apparatus, information processing method, and program |
US7428509B2 (en) * | 2002-01-10 | 2008-09-23 | Mastercard International Incorporated | Method and system for detecting payment account fraud |
US7580891B2 (en) * | 2002-01-10 | 2009-08-25 | Mastercard International Incorporated | Method and system for assisting in the identification of merchants at which payment accounts have been compromised |
US7716165B2 (en) * | 2002-02-12 | 2010-05-11 | Mantas, Inc. | Analysis of third party networks |
US7827080B2 (en) * | 2002-02-14 | 2010-11-02 | Multiple-Markets | Fixed income securities ratings visualization |
US8548885B2 (en) * | 2002-02-14 | 2013-10-01 | Multiple-Markets | Fixed income securities ratings visualization |
US7813937B1 (en) * | 2002-02-15 | 2010-10-12 | Fair Isaac Corporation | Consistency modeling of healthcare claims to detect fraud and abuse |
US7756896B1 (en) | 2002-03-11 | 2010-07-13 | Jp Morgan Chase Bank | System and method for multi-dimensional risk analysis |
WO2003079214A1 (en) * | 2002-03-11 | 2003-09-25 | Goldman, Sachs & Co. | Network access risk management |
US7899753B1 (en) | 2002-03-25 | 2011-03-01 | Jpmorgan Chase Bank, N.A | Systems and methods for time variable financial authentication |
US20030187783A1 (en) * | 2002-03-27 | 2003-10-02 | First Data Corporation | Systems and methods to monitor credit fraud |
US20030187759A1 (en) * | 2002-03-27 | 2003-10-02 | First Data Corporation | Systems and methods for electronically monitoring fraudulent activity |
US20040210498A1 (en) | 2002-03-29 | 2004-10-21 | Bank One, National Association | Method and system for performing purchase and other transactions using tokens with multiple chips |
US8751391B2 (en) | 2002-03-29 | 2014-06-10 | Jpmorgan Chase Bank, N.A. | System and process for performing purchase transactions using tokens |
US20030187766A1 (en) * | 2002-03-29 | 2003-10-02 | Nissho Iwai American Corporation | Automated risk management system and method |
US7039167B2 (en) * | 2002-04-04 | 2006-05-02 | Capitol One Financial Corporation | Call routing system and method |
US7698182B2 (en) * | 2002-04-29 | 2010-04-13 | Evercom Systems, Inc. | Optimizing profitability in business transactions |
US9026468B2 (en) | 2002-04-29 | 2015-05-05 | Securus Technologies, Inc. | System and method for proactively establishing a third-party payment account for services rendered to a resident of a controlled-environment facility |
US8255300B2 (en) | 2002-04-29 | 2012-08-28 | Securus Technologies, Inc. | System and method for independently authorizing auxiliary communication services |
US7860222B1 (en) | 2003-11-24 | 2010-12-28 | Securus Technologies, Inc. | Systems and methods for acquiring, accessing, and analyzing investigative information |
US8068590B1 (en) | 2002-04-29 | 2011-11-29 | Securus Technologies, Inc. | Optimizing profitability in business transactions |
US20050154688A1 (en) * | 2002-05-13 | 2005-07-14 | George Bolt | Automated performance monitoring and adaptation system |
GB0210938D0 (en) * | 2002-05-13 | 2002-06-19 | Neural Technologies Ltd | An automatic performance monitoring and adaptation system |
CA2485034C (en) * | 2002-05-16 | 2016-07-12 | Ndchealth Corporation | Systems and methods for verifying and editing electronically transmitted claim content |
US9569797B1 (en) | 2002-05-30 | 2017-02-14 | Consumerinfo.Com, Inc. | Systems and methods of presenting simulated credit score information |
US20030222138A1 (en) * | 2002-05-31 | 2003-12-04 | Carole Oppenlander | System and method for authorizing transactions |
WO2004001538A2 (en) * | 2002-06-20 | 2003-12-31 | Goldman, Sachs & Co. | Hedge fund risk management |
US8239304B1 (en) | 2002-07-29 | 2012-08-07 | Jpmorgan Chase Bank, N.A. | Method and system for providing pre-approved targeted products |
US20040030667A1 (en) * | 2002-08-02 | 2004-02-12 | Capital One Financial Corporation | Automated systems and methods for generating statistical models |
US20090210246A1 (en) * | 2002-08-19 | 2009-08-20 | Choicestream, Inc. | Statistical personalized recommendation system |
US20040049473A1 (en) * | 2002-09-05 | 2004-03-11 | David John Gower | Information analytics systems and methods |
WO2004025540A2 (en) * | 2002-09-13 | 2004-03-25 | United States Postal Services | Method for detecting suspicious transactions |
US7809595B2 (en) * | 2002-09-17 | 2010-10-05 | Jpmorgan Chase Bank, Na | System and method for managing risks associated with outside service providers |
US7716068B2 (en) | 2002-09-25 | 2010-05-11 | Mckesson Financial Holdings Limited | Systems and methods for look-alike sound-alike medication error messaging |
US20040064401A1 (en) * | 2002-09-27 | 2004-04-01 | Capital One Financial Corporation | Systems and methods for detecting fraudulent information |
JP3948389B2 (en) * | 2002-10-24 | 2007-07-25 | 富士ゼロックス株式会社 | Communication analyzer |
US7451095B1 (en) * | 2002-10-30 | 2008-11-11 | Freddie Mac | Systems and methods for income scoring |
US7797166B1 (en) | 2002-10-30 | 2010-09-14 | Federal Home Loan Mortgage Corporation (Freddie Mac) | Systems and methods for generating a model for income scoring |
US7702574B2 (en) * | 2002-11-14 | 2010-04-20 | Goldman Sachs & Co. | Independent research consensus earnings estimates and methods of determining such |
GB0228219D0 (en) * | 2002-12-04 | 2003-01-08 | Waterleaf Ltd | Collusion detection and control |
US7373664B2 (en) * | 2002-12-16 | 2008-05-13 | Symantec Corporation | Proactive protection against e-mail worms and spam |
US20040138975A1 (en) * | 2002-12-17 | 2004-07-15 | Lisa Engel | System and method for preventing fraud in check orders |
US20040116783A1 (en) * | 2002-12-17 | 2004-06-17 | International Business Machines Corporation | Behavior based life support generating new behavior patterns from behavior indicators for a user |
US20040116781A1 (en) * | 2002-12-17 | 2004-06-17 | International Business Machines Corporation | Behavior based life support generating new behavior patterns from historical behavior indicators |
US20040116102A1 (en) * | 2002-12-17 | 2004-06-17 | International Business Machines Corporation | Heuristics for behavior based life support services |
US20110202565A1 (en) * | 2002-12-31 | 2011-08-18 | American Express Travel Related Services Company, Inc. | Method and system for implementing and managing an enterprise identity management for distributed security in a computer system |
US7143095B2 (en) * | 2002-12-31 | 2006-11-28 | American Express Travel Related Services Company, Inc. | Method and system for implementing and managing an enterprise identity management for distributed security |
US9818136B1 (en) | 2003-02-05 | 2017-11-14 | Steven M. Hoffberg | System and method for determining contingent relevance |
GB0303053D0 (en) * | 2003-02-11 | 2003-03-19 | Waterleaf Ltd | Collusion detection |
US20040230448A1 (en) * | 2003-02-14 | 2004-11-18 | William Schaich | System for managing and reporting financial account activity |
US7505931B2 (en) * | 2003-03-03 | 2009-03-17 | Standard Chartered (Ct) Plc | Method and system for monitoring transactions |
US7693810B2 (en) * | 2003-03-04 | 2010-04-06 | Mantas, Inc. | Method and system for advanced scenario based alert generation and processing |
US7657474B1 (en) | 2003-03-04 | 2010-02-02 | Mantas, Inc. | Method and system for the detection of trading compliance violations for fixed income securities |
US20040199462A1 (en) * | 2003-04-02 | 2004-10-07 | Ed Starrs | Fraud control method and system for network transactions |
US20040215574A1 (en) * | 2003-04-25 | 2004-10-28 | First Data Corporation | Systems and methods for verifying identities in transactions |
US7844545B2 (en) * | 2003-04-25 | 2010-11-30 | The Western Union Company | Systems and methods for validating identifications in financial transactions |
US7686214B1 (en) * | 2003-05-12 | 2010-03-30 | Id Analytics, Inc. | System and method for identity-based fraud detection using a plurality of historical identity records |
US7458508B1 (en) | 2003-05-12 | 2008-12-02 | Id Analytics, Inc. | System and method for identity-based fraud detection |
US7562814B1 (en) * | 2003-05-12 | 2009-07-21 | Id Analytics, Inc. | System and method for identity-based fraud detection through graph anomaly detection |
US8386377B1 (en) | 2003-05-12 | 2013-02-26 | Id Analytics, Inc. | System and method for credit scoring using an identity network connectivity |
US10521857B1 (en) | 2003-05-12 | 2019-12-31 | Symantec Corporation | System and method for identity-based fraud detection |
EP1625543A4 (en) * | 2003-05-22 | 2008-03-12 | Pershing Investments Llc | Method and system for predicting attrition customers |
US8306907B2 (en) | 2003-05-30 | 2012-11-06 | Jpmorgan Chase Bank N.A. | System and method for offering risk-based interest rates in a credit instrument |
US9412123B2 (en) | 2003-07-01 | 2016-08-09 | The 41St Parameter, Inc. | Keystroke analysis |
US7370098B2 (en) * | 2003-08-06 | 2008-05-06 | International Business Machines Corporation | Autonomic management of autonomic systems |
US7561680B1 (en) | 2003-08-13 | 2009-07-14 | Evercom Systems, Inc. | System and method for called party controlled message delivery |
US7761374B2 (en) | 2003-08-18 | 2010-07-20 | Visa International Service Association | Method and system for generating a dynamic verification value |
US7740168B2 (en) | 2003-08-18 | 2010-06-22 | Visa U.S.A. Inc. | Method and system for generating a dynamic verification value |
US20050144143A1 (en) * | 2003-09-03 | 2005-06-30 | Steven Freiberg | Method and system for identity theft prevention, detection and victim assistance |
US20050055373A1 (en) * | 2003-09-04 | 2005-03-10 | Forman George H. | Determining point-of-compromise |
US7953663B1 (en) | 2003-09-04 | 2011-05-31 | Jpmorgan Chase Bank, N.A. | System and method for financial instrument pre-qualification and offering |
US8572391B2 (en) * | 2003-09-12 | 2013-10-29 | Emc Corporation | System and method for risk based authentication |
US20080270206A1 (en) * | 2003-09-13 | 2008-10-30 | United States Postal Service | Method for detecting suspicious transactions |
US20050075911A1 (en) * | 2003-10-03 | 2005-04-07 | Affiliated Flood Group, L.L.C. | Method for producing, selling, and delivering data required by mortgage lenders and servicers to comply with flood insurance monitoring requirements |
US7503488B2 (en) * | 2003-10-17 | 2009-03-17 | Davis Bruce L | Fraud prevention in issuance of identification credentials |
US7225977B2 (en) * | 2003-10-17 | 2007-06-05 | Digimarc Corporation | Fraud deterrence in connection with identity documents |
US20050097046A1 (en) | 2003-10-30 | 2005-05-05 | Singfield Joy S. | Wireless electronic check deposit scanning and cashing machine with web-based online account cash management computer application system |
US7287078B2 (en) * | 2003-10-31 | 2007-10-23 | Hewlett-Packard Development Company, L.P. | Restoration of lost peer-to-peer offline transaction records |
US20050097051A1 (en) * | 2003-11-05 | 2005-05-05 | Madill Robert P.Jr. | Fraud potential indicator graphical interface |
US20050108063A1 (en) | 2003-11-05 | 2005-05-19 | Madill Robert P.Jr. | Systems and methods for assessing the potential for fraud in business transactions |
US20050108178A1 (en) * | 2003-11-17 | 2005-05-19 | Richard York | Order risk determination |
US8458073B2 (en) * | 2003-12-02 | 2013-06-04 | Dun & Bradstreet, Inc. | Enterprise risk assessment manager system |
US20060247975A1 (en) * | 2003-12-30 | 2006-11-02 | Craig Shapiro | Processes and systems employing multiple sources of funds |
US20050144100A1 (en) * | 2003-12-30 | 2005-06-30 | Craig Shapiro | Payment systems and methods for earning incentives using at least two financial instruments |
US7716135B2 (en) * | 2004-01-29 | 2010-05-11 | International Business Machines Corporation | Incremental compliance environment, an enterprise-wide system for detecting fraud |
US9026467B2 (en) * | 2004-02-13 | 2015-05-05 | Fis Financial Compliance Solutions, Llc | Systems and methods for monitoring and detecting fraudulent uses of business applications |
US9978031B2 (en) | 2004-02-13 | 2018-05-22 | Fis Financial Compliance Solutions, Llc | Systems and methods for monitoring and detecting fraudulent uses of business applications |
US8612479B2 (en) * | 2004-02-13 | 2013-12-17 | Fis Financial Compliance Solutions, Llc | Systems and methods for monitoring and detecting fraudulent uses of business applications |
US10325272B2 (en) * | 2004-02-20 | 2019-06-18 | Information Resources, Inc. | Bias reduction using data fusion of household panel data and transaction data |
US10999298B2 (en) | 2004-03-02 | 2021-05-04 | The 41St Parameter, Inc. | Method and system for identifying users and detecting fraud by use of the internet |
US7708190B2 (en) * | 2004-03-10 | 2010-05-04 | At&T Intellectual Property I, L.P. | Multiple options to decline authorization of payment card charges |
US20050216397A1 (en) * | 2004-03-26 | 2005-09-29 | Clearcommerce, Inc. | Method, system, and computer program product for processing a financial transaction request |
IL161217A (en) * | 2004-04-01 | 2013-03-24 | Cvidya 2010 Ltd | Detection of outliers in communication networks |
US20050222929A1 (en) * | 2004-04-06 | 2005-10-06 | Pricewaterhousecoopers Llp | Systems and methods for investigation of financial reporting information |
US20050222928A1 (en) * | 2004-04-06 | 2005-10-06 | Pricewaterhousecoopers Llp | Systems and methods for investigation of financial reporting information |
US20050288952A1 (en) * | 2004-05-18 | 2005-12-29 | Davis Bruce L | Official documents and methods of issuance |
WO2005114886A2 (en) * | 2004-05-21 | 2005-12-01 | Rsa Security Inc. | System and method of fraud reduction |
US7954698B1 (en) | 2004-06-02 | 2011-06-07 | Pliha Robert K | System and method for matching customers to financial products, services, and incentives based on bank account transaction activity |
US7272728B2 (en) * | 2004-06-14 | 2007-09-18 | Iovation, Inc. | Network security and fraud detection system and method |
US8082207B2 (en) * | 2004-06-17 | 2011-12-20 | Certegy Check Services, Inc. | Scored negative file system and method |
US8346593B2 (en) | 2004-06-30 | 2013-01-01 | Experian Marketing Solutions, Inc. | System, method, and software for prediction of attitudinal and message responsiveness |
US8762191B2 (en) | 2004-07-02 | 2014-06-24 | Goldman, Sachs & Co. | Systems, methods, apparatus, and schema for storing, managing and retrieving information |
US8996481B2 (en) | 2004-07-02 | 2015-03-31 | Goldman, Sach & Co. | Method, system, apparatus, program code and means for identifying and extracting information |
US8442953B2 (en) | 2004-07-02 | 2013-05-14 | Goldman, Sachs & Co. | Method, system, apparatus, program code and means for determining a redundancy of information |
US8510300B2 (en) | 2004-07-02 | 2013-08-13 | Goldman, Sachs & Co. | Systems and methods for managing information associated with legal, compliance and regulatory risk |
US7392222B1 (en) | 2004-08-03 | 2008-06-24 | Jpmorgan Chase Bank, N.A. | System and method for providing promotional pricing |
US20060041464A1 (en) * | 2004-08-19 | 2006-02-23 | Transunion Llc. | System and method for developing an analytic fraud model |
US8914309B2 (en) * | 2004-08-20 | 2014-12-16 | Ebay Inc. | Method and system for tracking fraudulent activity |
US7590589B2 (en) | 2004-09-10 | 2009-09-15 | Hoffberg Steven M | Game theoretic prioritization scheme for mobile ad hoc networks permitting hierarchal deference |
US7497374B2 (en) * | 2004-09-17 | 2009-03-03 | Digital Envoy, Inc. | Fraud risk advisor |
US7870047B2 (en) * | 2004-09-17 | 2011-01-11 | International Business Machines Corporation | System, method for deploying computing infrastructure, and method for identifying customers at risk of revenue change |
US7543740B2 (en) * | 2004-09-17 | 2009-06-09 | Digital Envoy, Inc. | Fraud analyst smart cookie |
US8732004B1 (en) | 2004-09-22 | 2014-05-20 | Experian Information Solutions, Inc. | Automated analysis of data to generate prospect notifications based on trigger events |
US7877309B2 (en) | 2004-10-18 | 2011-01-25 | Starmine Corporation | System and method for analyzing analyst recommendations on a single stock basis |
US8543499B2 (en) | 2004-10-29 | 2013-09-24 | American Express Travel Related Services Company, Inc. | Reducing risks related to check verification |
US8204774B2 (en) | 2004-10-29 | 2012-06-19 | American Express Travel Related Services Company, Inc. | Estimating the spend capacity of consumer households |
US8326672B2 (en) * | 2004-10-29 | 2012-12-04 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet in financial databases |
US7814004B2 (en) * | 2004-10-29 | 2010-10-12 | American Express Travel Related Services Company, Inc. | Method and apparatus for development and use of a credit score based on spend capacity |
US7912770B2 (en) * | 2004-10-29 | 2011-03-22 | American Express Travel Related Services Company, Inc. | Method and apparatus for consumer interaction based on spend capacity |
US20070226114A1 (en) * | 2004-10-29 | 2007-09-27 | American Express Travel Related Services Co., Inc., A New York Corporation | Using commercial share of wallet to manage investments |
US8630929B2 (en) * | 2004-10-29 | 2014-01-14 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to make lending decisions |
US20070244732A1 (en) * | 2004-10-29 | 2007-10-18 | American Express Travel Related Services Co., Inc., A New York Corporation | Using commercial share of wallet to manage vendors |
US20060242048A1 (en) * | 2004-10-29 | 2006-10-26 | American Express Travel Related Services Company, Inc. | Method and apparatus for determining credit characteristics of a consumer |
US7610243B2 (en) * | 2004-10-29 | 2009-10-27 | American Express Travel Related Services Company, Inc. | Method and apparatus for rating asset-backed securities |
US7788147B2 (en) * | 2004-10-29 | 2010-08-31 | American Express Travel Related Services Company, Inc. | Method and apparatus for estimating the spend capacity of consumers |
US7822665B2 (en) * | 2004-10-29 | 2010-10-26 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet in private equity investments |
US7840484B2 (en) * | 2004-10-29 | 2010-11-23 | American Express Travel Related Services Company, Inc. | Credit score and scorecard development |
US20070016500A1 (en) * | 2004-10-29 | 2007-01-18 | American Express Travel Related Services Co., Inc. A New York Corporation | Using commercial share of wallet to determine insurance risk |
US8326671B2 (en) * | 2004-10-29 | 2012-12-04 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to analyze vendors in online marketplaces |
US8086509B2 (en) | 2004-10-29 | 2011-12-27 | American Express Travel Related Services Company, Inc. | Determining commercial share of wallet |
US7792732B2 (en) | 2004-10-29 | 2010-09-07 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to rate investments |
US20060242050A1 (en) * | 2004-10-29 | 2006-10-26 | American Express Travel Related Services Company, Inc. | Method and apparatus for targeting best customers based on spend capacity |
US8131614B2 (en) * | 2004-10-29 | 2012-03-06 | American Express Travel Related Services Company, Inc. | Using commercial share of wallet to compile marketing company lists |
US20070016501A1 (en) | 2004-10-29 | 2007-01-18 | American Express Travel Related Services Co., Inc., A New York Corporation | Using commercial share of wallet to rate business prospects |
FI20041417A (en) * | 2004-11-04 | 2006-05-05 | Xtract Oy | Personal unit, processing device and method for event authentication |
US7693765B2 (en) * | 2004-11-30 | 2010-04-06 | Michael Dell Orfano | System and method for creating electronic real estate registration |
US9076185B2 (en) * | 2004-11-30 | 2015-07-07 | Michael Dell Orfano | System and method for managing electronic real estate registry information |
US20060149674A1 (en) * | 2004-12-30 | 2006-07-06 | Mike Cook | System and method for identity-based fraud detection for transactions using a plurality of historical identity records |
US7802722B1 (en) | 2004-12-31 | 2010-09-28 | Teradata Us, Inc. | Techniques for managing fraud information |
US8639629B1 (en) | 2005-02-02 | 2014-01-28 | Nexus Payments, LLC | System and method for accessing an online user account registry via a thin-client unique user code |
US8768838B1 (en) | 2005-02-02 | 2014-07-01 | Nexus Payments, LLC | Financial transactions using a rule-module nexus and a user account registry |
US11288666B1 (en) | 2005-02-02 | 2022-03-29 | Edge Mobile Payments Llc | System and method for real-time processing of on-line financial transactions using a universal financial token and a remotely located rule-module clearinghouse |
US7860812B2 (en) * | 2005-03-02 | 2010-12-28 | Accenture Global Services Limited | Advanced insurance record audit and payment integrity |
US20060212356A1 (en) * | 2005-03-14 | 2006-09-21 | Michael Lambert | System and method for integrated order and channel management |
EP1710763A1 (en) * | 2005-04-08 | 2006-10-11 | First Data Corporation | System and method for authorizing electronic payment transactions |
US7630924B1 (en) * | 2005-04-20 | 2009-12-08 | Authorize.Net Llc | Transaction velocity counting for fraud detection |
US7779456B2 (en) * | 2005-04-27 | 2010-08-17 | Gary M Dennis | System and method for enhanced protection and control over the use of identity |
US7401731B1 (en) | 2005-05-27 | 2008-07-22 | Jpmorgan Chase Bank, Na | Method and system for implementing a card product with multiple customized relationships |
US8321283B2 (en) | 2005-05-27 | 2012-11-27 | Per-Se Technologies | Systems and methods for alerting pharmacies of formulary alternatives |
US20080021801A1 (en) * | 2005-05-31 | 2008-01-24 | Yuh-Shen Song | Dynamic multidimensional risk-weighted suspicious activities detector |
US7849029B2 (en) * | 2005-06-02 | 2010-12-07 | Fair Isaac Corporation | Comprehensive identity protection system |
US20060282375A1 (en) * | 2005-06-09 | 2006-12-14 | Valued Services Intellectual Property Management, | Credit underwriting for multiple financial transactions |
US20060287946A1 (en) * | 2005-06-16 | 2006-12-21 | Toms Alvin D | Loss management system and method |
US20060294084A1 (en) * | 2005-06-28 | 2006-12-28 | Patel Jayendu S | Methods and apparatus for a statistical system for targeting advertisements |
US20060293950A1 (en) * | 2005-06-28 | 2006-12-28 | Microsoft Corporation | Automatic ad placement |
JP2007011990A (en) * | 2005-07-04 | 2007-01-18 | Hitachi Ltd | Business portfolio simulation system |
US20070016521A1 (en) * | 2005-07-15 | 2007-01-18 | Zhiping Wang | Data processing system for a billing address-based credit watch |
WO2007028048A2 (en) * | 2005-09-02 | 2007-03-08 | Fair Isaac Corporation | Systems and methods for detecting fraud |
US9432468B2 (en) | 2005-09-14 | 2016-08-30 | Liveperson, Inc. | System and method for design and dynamic generation of a web page |
US8738732B2 (en) | 2005-09-14 | 2014-05-27 | Liveperson, Inc. | System and method for performing follow up based on user interactions |
US7668769B2 (en) * | 2005-10-04 | 2010-02-23 | Basepoint Analytics, LLC | System and method of detecting fraud |
US8874477B2 (en) | 2005-10-04 | 2014-10-28 | Steven Mark Hoffberg | Multifactorial optimization system and method |
WO2007042062A1 (en) * | 2005-10-12 | 2007-04-19 | First Data Corporation | System and method for authorizing electronic payment transactions |
US7552865B2 (en) * | 2005-10-20 | 2009-06-30 | Satyam Computer Services Ltd. | System and method for deep interaction modeling for fraud detection |
US20080033852A1 (en) * | 2005-10-24 | 2008-02-07 | Megdal Myles G | Computer-based modeling of spending behaviors of entities |
US20070162303A1 (en) | 2005-12-08 | 2007-07-12 | Ndchealth Corporation | Systems and Methods for Shifting Prescription Market Share by Presenting Pricing Differentials for Therapeutic Alternatives |
US11301585B2 (en) | 2005-12-16 | 2022-04-12 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
US8938671B2 (en) | 2005-12-16 | 2015-01-20 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
EP1960959A4 (en) * | 2005-12-16 | 2011-07-27 | Apex Analytix Inc | Systems and methods for automated vendor risk analysis |
US20090222363A1 (en) * | 2005-12-16 | 2009-09-03 | Arnold James B | Systems And Methods For Automated Retail Recovery Auditing |
US8244532B1 (en) | 2005-12-23 | 2012-08-14 | At&T Intellectual Property Ii, L.P. | Systems, methods, and programs for detecting unauthorized use of text based communications services |
US8280805B1 (en) | 2006-01-10 | 2012-10-02 | Sas Institute Inc. | Computer-implemented risk evaluation systems and methods |
EP1816595A1 (en) | 2006-02-06 | 2007-08-08 | MediaKey Ltd. | A method and a system for identifying potentially fraudulent customers in relation to network based commerce activities, in particular involving payment, and a computer program for performing said method |
US8408455B1 (en) | 2006-02-08 | 2013-04-02 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to both customers and non-customers |
US7784682B2 (en) | 2006-02-08 | 2010-08-31 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to both customers and non-customers |
US10127554B2 (en) * | 2006-02-15 | 2018-11-13 | Citibank, N.A. | Fraud early warning system and method |
US8567669B2 (en) * | 2006-02-24 | 2013-10-29 | Fair Isaac Corporation | Method and apparatus for a merchant profile builder |
WO2007101074A2 (en) * | 2006-02-24 | 2007-09-07 | Fair Isaac Corporation | Method and apparatus for a merchant profile builder |
US20070204033A1 (en) * | 2006-02-24 | 2007-08-30 | James Bookbinder | Methods and systems to detect abuse of network services |
US7711636B2 (en) | 2006-03-10 | 2010-05-04 | Experian Information Solutions, Inc. | Systems and methods for analyzing data |
US7912773B1 (en) * | 2006-03-24 | 2011-03-22 | Sas Institute Inc. | Computer-implemented data storage systems and methods for use with predictive model systems |
US7966256B2 (en) | 2006-09-22 | 2011-06-21 | Corelogic Information Solutions, Inc. | Methods and systems of predicting mortgage payment risk |
US7587348B2 (en) * | 2006-03-24 | 2009-09-08 | Basepoint Analytics Llc | System and method of detecting mortgage related fraud |
US7604541B2 (en) * | 2006-03-31 | 2009-10-20 | Information Extraction Transport, Inc. | System and method for detecting collusion in online gaming via conditional behavior |
US8151327B2 (en) | 2006-03-31 | 2012-04-03 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US7818264B2 (en) | 2006-06-19 | 2010-10-19 | Visa U.S.A. Inc. | Track data encryption |
US9065643B2 (en) | 2006-04-05 | 2015-06-23 | Visa U.S.A. Inc. | System and method for account identifier obfuscation |
US7753259B1 (en) | 2006-04-13 | 2010-07-13 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to both customers and non-customers |
US7849030B2 (en) * | 2006-05-31 | 2010-12-07 | Hartford Fire Insurance Company | Method and system for classifying documents |
US7804941B2 (en) * | 2006-06-30 | 2010-09-28 | Securus Technologies, Inc. | Systems and methods for message delivery in a controlled environment facility |
US10152736B2 (en) * | 2006-07-06 | 2018-12-11 | Fair Isaac Corporation | Auto adaptive anomaly detection system for streams |
US20080027881A1 (en) * | 2006-07-07 | 2008-01-31 | Stephan Kurt Jan Bisse | Market trader training tool |
US7849436B2 (en) * | 2006-08-11 | 2010-12-07 | Dongbu Hitek Co., Ltd. | Method of forming dummy pattern |
US20070100773A1 (en) * | 2006-08-11 | 2007-05-03 | Regions Asset Company | Transaction security system having user defined security parameters |
EP2074572A4 (en) | 2006-08-17 | 2011-02-23 | Experian Inf Solutions Inc | System and method for providing a score for a used vehicle |
US20080140564A1 (en) * | 2006-09-28 | 2008-06-12 | Yuval Tal | System and method for payment transfer |
US8036979B1 (en) | 2006-10-05 | 2011-10-11 | Experian Information Solutions, Inc. | System and method for generating a finance attribute from tradeline data |
US8239677B2 (en) | 2006-10-10 | 2012-08-07 | Equifax Inc. | Verification and authentication systems and methods |
US7739189B1 (en) | 2006-10-20 | 2010-06-15 | Fannie Mae | Method and system for detecting loan fraud |
US8751815B2 (en) * | 2006-10-25 | 2014-06-10 | Iovation Inc. | Creating and verifying globally unique device-specific identifiers |
US7873200B1 (en) | 2006-10-31 | 2011-01-18 | United Services Automobile Association (Usaa) | Systems and methods for remote deposit of checks |
US8351677B1 (en) | 2006-10-31 | 2013-01-08 | United Services Automobile Association (Usaa) | Systems and methods for remote deposit of checks |
US8799147B1 (en) | 2006-10-31 | 2014-08-05 | United Services Automobile Association (Usaa) | Systems and methods for remote deposit of negotiable instruments with non-payee institutions |
US8708227B1 (en) | 2006-10-31 | 2014-04-29 | United Services Automobile Association (Usaa) | Systems and methods for remote deposit of checks |
US7657497B2 (en) | 2006-11-07 | 2010-02-02 | Ebay Inc. | Online fraud prevention using genetic algorithm solution |
US8296223B2 (en) * | 2006-11-07 | 2012-10-23 | Federal Reserve Bank Of Atlanta | System and method for processing duplicative electronic check reversal files |
US7783565B1 (en) | 2006-11-08 | 2010-08-24 | Fannie Mae | Method and system for assessing repurchase risk |
US7752112B2 (en) | 2006-11-09 | 2010-07-06 | Starmine Corporation | System and method for using analyst data to identify peer securities |
US8738456B2 (en) * | 2006-11-14 | 2014-05-27 | Xerox Corporation | Electronic shopper catalog |
US7945037B1 (en) | 2006-11-22 | 2011-05-17 | Securus Technologies, Inc. | System and method for remote call forward detection using signaling |
US7657569B1 (en) | 2006-11-28 | 2010-02-02 | Lower My Bills, Inc. | System and method of removing duplicate leads |
US8239250B2 (en) * | 2006-12-01 | 2012-08-07 | American Express Travel Related Services Company, Inc. | Industry size of wallet |
US7778885B1 (en) | 2006-12-04 | 2010-08-17 | Lower My Bills, Inc. | System and method of enhancing leads |
US7673797B2 (en) * | 2006-12-13 | 2010-03-09 | Ncr Corporation | Personalization of self-checkout security |
US8359209B2 (en) | 2006-12-19 | 2013-01-22 | Hartford Fire Insurance Company | System and method for predicting and responding to likelihood of volatility |
US20080154664A1 (en) * | 2006-12-21 | 2008-06-26 | Calvin Kuo | System for generating scores related to interactions with a revenue generator |
US7945497B2 (en) * | 2006-12-22 | 2011-05-17 | Hartford Fire Insurance Company | System and method for utilizing interrelated computerized predictive models |
US20080162202A1 (en) * | 2006-12-29 | 2008-07-03 | Richendra Khanna | Detecting inappropriate activity by analysis of user interactions |
US8290838B1 (en) * | 2006-12-29 | 2012-10-16 | Amazon Technologies, Inc. | Indicating irregularities in online financial transactions |
US8175989B1 (en) | 2007-01-04 | 2012-05-08 | Choicestream, Inc. | Music recommendation system using a personalized choice set |
US8036967B2 (en) * | 2007-01-12 | 2011-10-11 | Allegacy Federal Credit Union | Bank card fraud detection and/or prevention methods |
US8818904B2 (en) | 2007-01-17 | 2014-08-26 | The Western Union Company | Generation systems and methods for transaction identifiers having biometric keys associated therewith |
US7933835B2 (en) | 2007-01-17 | 2011-04-26 | The Western Union Company | Secure money transfer systems and methods using biometric keys associated therewith |
US8504598B2 (en) | 2007-01-26 | 2013-08-06 | Information Resources, Inc. | Data perturbation of non-unique values |
EP2111593A2 (en) * | 2007-01-26 | 2009-10-28 | Information Resources, Inc. | Analytic platform |
US20080263000A1 (en) * | 2007-01-26 | 2008-10-23 | John Randall West | Utilizing aggregated data |
US8160984B2 (en) | 2007-01-26 | 2012-04-17 | Symphonyiri Group, Inc. | Similarity matching of a competitor's products |
US8606626B1 (en) | 2007-01-31 | 2013-12-10 | Experian Information Solutions, Inc. | Systems and methods for providing a direct marketing campaign planning environment |
US8606666B1 (en) | 2007-01-31 | 2013-12-10 | Experian Information Solutions, Inc. | System and method for providing an aggregation tool |
US20080191007A1 (en) * | 2007-02-13 | 2008-08-14 | First Data Corporation | Methods and Systems for Identifying Fraudulent Transactions Across Multiple Accounts |
US8015133B1 (en) | 2007-02-20 | 2011-09-06 | Sas Institute Inc. | Computer-implemented modeling systems and methods for analyzing and predicting computer network intrusions |
US8346691B1 (en) | 2007-02-20 | 2013-01-01 | Sas Institute Inc. | Computer-implemented semi-supervised learning systems and methods |
US8190512B1 (en) | 2007-02-20 | 2012-05-29 | Sas Institute Inc. | Computer-implemented clustering systems and methods for action determination |
US8959033B1 (en) | 2007-03-15 | 2015-02-17 | United Services Automobile Association (Usaa) | Systems and methods for verification of remotely deposited checks |
US10380559B1 (en) | 2007-03-15 | 2019-08-13 | United Services Automobile Association (Usaa) | Systems and methods for check representment prevention |
US8280348B2 (en) | 2007-03-16 | 2012-10-02 | Finsphere Corporation | System and method for identity protection using mobile device signaling network derived location pattern recognition |
US9185123B2 (en) | 2008-02-12 | 2015-11-10 | Finsphere Corporation | System and method for mobile identity protection for online user authentication |
US8374634B2 (en) | 2007-03-16 | 2013-02-12 | Finsphere Corporation | System and method for automated analysis comparing a wireless device location with another geographic location |
US8116731B2 (en) * | 2007-11-01 | 2012-02-14 | Finsphere, Inc. | System and method for mobile identity protection of a user of multiple computer applications, networks or devices |
US8504473B2 (en) | 2007-03-28 | 2013-08-06 | The Western Union Company | Money transfer system and messaging system |
US10395309B2 (en) * | 2007-03-30 | 2019-08-27 | Detica Patent Limited | Detection of activity patterns |
US20090018940A1 (en) * | 2007-03-30 | 2009-01-15 | Liang Wang | Enhanced Fraud Detection With Terminal Transaction-Sequence Processing |
US7975299B1 (en) | 2007-04-05 | 2011-07-05 | Consumerinfo.Com, Inc. | Child identity monitor |
WO2008127288A1 (en) | 2007-04-12 | 2008-10-23 | Experian Information Solutions, Inc. | Systems and methods for determining thin-file records and determining thin-file risk levels |
US8191053B2 (en) * | 2007-04-12 | 2012-05-29 | Ingenix, Inc. | Computerized data warehousing |
US8010502B2 (en) * | 2007-04-13 | 2011-08-30 | Harris Corporation | Methods and systems for data recovery |
US20100106586A1 (en) * | 2007-04-17 | 2010-04-29 | American Express Travel Related Services Company, Inc. | System and method for determining positive consumer behavior based upon structural risk |
US7853526B2 (en) * | 2007-05-04 | 2010-12-14 | Fair Isaac Corporation | Data transaction profile compression |
US8538124B1 (en) | 2007-05-10 | 2013-09-17 | United Services Auto Association (USAA) | Systems and methods for real-time validation of check image quality |
US8433127B1 (en) | 2007-05-10 | 2013-04-30 | United Services Automobile Association (Usaa) | Systems and methods for real-time validation of check image quality |
US20080283593A1 (en) * | 2007-05-18 | 2008-11-20 | Bank Of America Corporation | Compromised Account Detection |
US10796392B1 (en) | 2007-05-22 | 2020-10-06 | Securus Technologies, Llc | Systems and methods for facilitating booking, bonding and release |
US20080294540A1 (en) | 2007-05-25 | 2008-11-27 | Celka Christopher J | System and method for automated detection of never-pay data sets |
US8165938B2 (en) * | 2007-06-04 | 2012-04-24 | Visa U.S.A. Inc. | Prepaid card fraud and risk management |
US7627522B2 (en) * | 2007-06-04 | 2009-12-01 | Visa U.S.A. Inc. | System, apparatus and methods for comparing fraud parameters for application during prepaid card enrollment and transactions |
US7739169B2 (en) * | 2007-06-25 | 2010-06-15 | Visa U.S.A. Inc. | Restricting access to compromised account information |
US8121942B2 (en) | 2007-06-25 | 2012-02-21 | Visa U.S.A. Inc. | Systems and methods for secure and transparent cardless transactions |
US8676642B1 (en) | 2007-07-05 | 2014-03-18 | Jpmorgan Chase Bank, N.A. | System and method for granting promotional rewards to financial account holders |
US20090030710A1 (en) * | 2007-07-27 | 2009-01-29 | Visa U.S.A. Inc. | Centralized dispute resolution system for commercial transactions |
US8600872B1 (en) | 2007-07-27 | 2013-12-03 | Wells Fargo Bank, N.A. | System and method for detecting account compromises |
US20090043615A1 (en) * | 2007-08-07 | 2009-02-12 | Hartford Fire Insurance Company | Systems and methods for predictive data analysis |
US8086524B1 (en) | 2007-09-10 | 2011-12-27 | Patrick James Craig | Systems and methods for transaction processing and balance transfer processing |
US8301574B2 (en) | 2007-09-17 | 2012-10-30 | Experian Marketing Solutions, Inc. | Multimedia engagement study |
US9690820B1 (en) | 2007-09-27 | 2017-06-27 | Experian Information Solutions, Inc. | Database system for triggering event notifications based on updates to database records |
US9058512B1 (en) | 2007-09-28 | 2015-06-16 | United Services Automobile Association (Usaa) | Systems and methods for digital signature detection |
US20090106151A1 (en) * | 2007-10-17 | 2009-04-23 | Mark Allen Nelsen | Fraud prevention based on risk assessment rule |
US8417601B1 (en) | 2007-10-18 | 2013-04-09 | Jpmorgan Chase Bank, N.A. | Variable rate payment card |
US9846884B2 (en) | 2007-10-19 | 2017-12-19 | Fair Isaac Corporation | Click conversion score |
US9898778B1 (en) | 2007-10-23 | 2018-02-20 | United Services Automobile Association (Usaa) | Systems and methods for obtaining an image of a check to be deposited |
US8358826B1 (en) | 2007-10-23 | 2013-01-22 | United Services Automobile Association (Usaa) | Systems and methods for receiving and orienting an image of one or more checks |
US9892454B1 (en) | 2007-10-23 | 2018-02-13 | United Services Automobile Association (Usaa) | Systems and methods for obtaining an image of a check to be deposited |
US9159101B1 (en) | 2007-10-23 | 2015-10-13 | United Services Automobile Association (Usaa) | Image processing |
US7865439B2 (en) * | 2007-10-24 | 2011-01-04 | The Western Union Company | Systems and methods for verifying identities |
US8320657B1 (en) | 2007-10-31 | 2012-11-27 | United Services Automobile Association (Usaa) | Systems and methods to use a digital camera to remotely deposit a negotiable instrument |
US8290237B1 (en) | 2007-10-31 | 2012-10-16 | United Services Automobile Association (Usaa) | Systems and methods to use a digital camera to remotely deposit a negotiable instrument |
US7900822B1 (en) | 2007-11-06 | 2011-03-08 | United Services Automobile Association (Usaa) | Systems, methods, and apparatus for receiving images of one or more checks |
US8791948B2 (en) * | 2007-11-09 | 2014-07-29 | Ebay Inc. | Methods and systems to generate graphical representations of relationships between persons based on transactions |
US8775475B2 (en) * | 2007-11-09 | 2014-07-08 | Ebay Inc. | Transaction data representations using an adjacency matrix |
US7440915B1 (en) | 2007-11-16 | 2008-10-21 | U.S. Bancorp Licensing, Inc. | Method, system, and computer-readable medium for reducing payee fraud |
US7996521B2 (en) | 2007-11-19 | 2011-08-09 | Experian Marketing Solutions, Inc. | Service for mapping IP addresses to user segments |
EP2085921A4 (en) * | 2007-11-28 | 2011-11-02 | Intelligent Wave Inc | Settlement approval system and settlement approval method of credit card |
US8046324B2 (en) * | 2007-11-30 | 2011-10-25 | Ebay Inc. | Graph pattern recognition interface |
US20090164376A1 (en) * | 2007-12-20 | 2009-06-25 | Mckesson Financial Holdings Limited | Systems and Methods for Controlled Substance Prescription Monitoring Via Real Time Claims Network |
US20120030115A1 (en) * | 2008-01-04 | 2012-02-02 | Deborah Peace | Systems and methods for preventing fraudulent banking transactions |
US10380562B1 (en) | 2008-02-07 | 2019-08-13 | United Services Automobile Association (Usaa) | Systems and methods for mobile deposit of negotiable instruments |
US8078528B1 (en) | 2008-02-21 | 2011-12-13 | Jpmorgan Chase Bank, N.A. | System and method for providing borrowing schemes |
US20090222308A1 (en) * | 2008-03-03 | 2009-09-03 | Zoldi Scott M | Detecting first party fraud abuse |
US7707089B1 (en) * | 2008-03-12 | 2010-04-27 | Jpmorgan Chase, N.A. | Method and system for automating fraud authorization strategies |
US8078569B2 (en) * | 2008-03-26 | 2011-12-13 | Fair Isaac Corporation | Estimating transaction risk using sub-models characterizing cross-interaction among categorical and non-categorical variables |
US7882027B2 (en) * | 2008-03-28 | 2011-02-01 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US7877323B2 (en) | 2008-03-28 | 2011-01-25 | American Express Travel Related Services Company, Inc. | Consumer behaviors at lender level |
US8635083B1 (en) | 2008-04-02 | 2014-01-21 | Mckesson Financial Holdings | Systems and methods for facilitating the establishment of pharmaceutical rebate agreements |
US9378527B2 (en) * | 2008-04-08 | 2016-06-28 | Hartford Fire Insurance Company | Computer system for applying predictive model to determine and indeterminate data |
US20090276346A1 (en) * | 2008-05-02 | 2009-11-05 | Intuit Inc. | System and method for classifying a financial transaction as a recurring financial transaction |
US20090292568A1 (en) * | 2008-05-22 | 2009-11-26 | Reza Khosravani | Adaptive Risk Variables |
US8521631B2 (en) | 2008-05-29 | 2013-08-27 | Sas Institute Inc. | Computer-implemented systems and methods for loan evaluation using a credit assessment framework |
US20090307049A1 (en) * | 2008-06-05 | 2009-12-10 | Fair Isaac Corporation | Soft Co-Clustering of Data |
US20090307140A1 (en) * | 2008-06-06 | 2009-12-10 | Upendra Mardikar | Mobile device over-the-air (ota) registration and point-of-sale (pos) payment |
US8351678B1 (en) | 2008-06-11 | 2013-01-08 | United Services Automobile Association (Usaa) | Duplicate check detection |
US20090313156A1 (en) * | 2008-06-12 | 2009-12-17 | Wachovia Corporation | Adaptive daily spending limits |
US8280833B2 (en) | 2008-06-12 | 2012-10-02 | Guardian Analytics, Inc. | Fraud detection and analysis |
US10373198B1 (en) | 2008-06-13 | 2019-08-06 | Lmb Mortgage Services, Inc. | System and method of generating existing customer leads |
US8626525B2 (en) * | 2008-06-23 | 2014-01-07 | Mckesson Financial Holdings | Systems and methods for real-time monitoring and analysis of prescription claim rejections |
US20090319287A1 (en) * | 2008-06-24 | 2009-12-24 | Ayman Hammad | Authentication segmentation |
US8538777B1 (en) | 2008-06-30 | 2013-09-17 | Mckesson Financial Holdings Limited | Systems and methods for providing patient medication history |
US20090327363A1 (en) * | 2008-06-30 | 2009-12-31 | Peter Cullen | Systems and methods for processing electronically transmitted healthcare related transactions |
US20090326977A1 (en) * | 2008-06-30 | 2009-12-31 | Mckesson Financial Holding Limited | Systems and Methods for Providing Drug Samples to Patients |
US20090327107A1 (en) * | 2008-06-30 | 2009-12-31 | Raghav Lal | Consumer spending threshold evaluation |
US20100005029A1 (en) * | 2008-07-03 | 2010-01-07 | Mark Allen Nelsen | Risk management workstation |
AU2009201514A1 (en) * | 2008-07-11 | 2010-01-28 | Icyte Pty Ltd | Annotation system and method |
US7991689B1 (en) | 2008-07-23 | 2011-08-02 | Experian Information Solutions, Inc. | Systems and methods for detecting bust out fraud using credit data |
US8762313B2 (en) | 2008-07-25 | 2014-06-24 | Liveperson, Inc. | Method and system for creating a predictive model for targeting web-page to a surfer |
US8260846B2 (en) | 2008-07-25 | 2012-09-04 | Liveperson, Inc. | Method and system for providing targeted content to a surfer |
US8805844B2 (en) | 2008-08-04 | 2014-08-12 | Liveperson, Inc. | Expert search |
US7925559B2 (en) * | 2008-08-22 | 2011-04-12 | Hartford Fire Insurance Company | Computer system for applying proactive referral model to long term disability claims |
US8422758B1 (en) | 2008-09-02 | 2013-04-16 | United Services Automobile Association (Usaa) | Systems and methods of check re-presentment deterrent |
US10504185B1 (en) | 2008-09-08 | 2019-12-10 | United Services Automobile Association (Usaa) | Systems and methods for live video financial deposit |
US8429194B2 (en) | 2008-09-15 | 2013-04-23 | Palantir Technologies, Inc. | Document-based workflows |
US8391599B1 (en) | 2008-10-17 | 2013-03-05 | United Services Automobile Association (Usaa) | Systems and methods for adaptive binarization of an image |
KR101545582B1 (en) * | 2008-10-29 | 2015-08-19 | 엘지전자 주식회사 | Terminal and method for controlling the same |
US9892417B2 (en) | 2008-10-29 | 2018-02-13 | Liveperson, Inc. | System and method for applying tracing tools for network locations |
BRPI0921124A2 (en) | 2008-11-06 | 2016-09-13 | Visa Int Service Ass | system for authenticating a consumer, computer implemented method, computer readable medium, and server computer. |
KR101064908B1 (en) * | 2008-11-12 | 2011-09-16 | 연세대학교 산학협력단 | Method for patterning nanowires on substrate using novel sacrificial layer material |
US9031866B1 (en) | 2008-11-17 | 2015-05-12 | Jpmorgan Chase Bank, N.A. | Systems and methods for use of transaction data for customers |
US20100174638A1 (en) | 2009-01-06 | 2010-07-08 | ConsumerInfo.com | Report existence monitoring |
US8762239B2 (en) * | 2009-01-12 | 2014-06-24 | Visa U.S.A. Inc. | Non-financial transactions in a financial transaction network |
US8494942B2 (en) * | 2009-01-15 | 2013-07-23 | First Data Corporation | Identifying and sharing common trends |
US8046242B1 (en) | 2009-01-22 | 2011-10-25 | Mckesson Financial Holdings Limited | Systems and methods for verifying prescription dosages |
US8452689B1 (en) | 2009-02-18 | 2013-05-28 | United Services Automobile Association (Usaa) | Systems and methods of check detection |
US8090648B2 (en) * | 2009-03-04 | 2012-01-03 | Fair Isaac Corporation | Fraud detection based on efficient frequent-behavior sorted lists |
US10956728B1 (en) | 2009-03-04 | 2021-03-23 | United Services Automobile Association (Usaa) | Systems and methods of check processing with background removal |
US20100235909A1 (en) * | 2009-03-13 | 2010-09-16 | Silver Tail Systems | System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Velocity Analysis |
US20100235908A1 (en) * | 2009-03-13 | 2010-09-16 | Silver Tail Systems | System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Analysis |
US9112850B1 (en) | 2009-03-25 | 2015-08-18 | The 41St Parameter, Inc. | Systems and methods of sharing information through a tag-based consortium |
US8380569B2 (en) * | 2009-04-16 | 2013-02-19 | Visa International Service Association, Inc. | Method and system for advanced warning alerts using advanced identification system for identifying fraud detection and reporting |
US20100274691A1 (en) * | 2009-04-28 | 2010-10-28 | Ayman Hammad | Multi alerts based system |
WO2010127216A2 (en) * | 2009-05-01 | 2010-11-04 | Telcodia Technologies, Inc. | Automated determination of quasi-identifiers using program analysis |
AU2010246077B2 (en) * | 2009-05-04 | 2013-09-05 | Visa International Service Association | Frequency-based transaction prediction and processing |
US8924279B2 (en) | 2009-05-07 | 2014-12-30 | Visa U.S.A. Inc. | Risk assessment rule set application for fraud prevention |
WO2010132492A2 (en) | 2009-05-11 | 2010-11-18 | Experian Marketing Solutions, Inc. | Systems and methods for providing anonymized user profile data |
US8600873B2 (en) * | 2009-05-28 | 2013-12-03 | Visa International Service Association | Managed real-time transaction fraud analysis and decisioning |
US10290053B2 (en) | 2009-06-12 | 2019-05-14 | Guardian Analytics, Inc. | Fraud detection and analysis |
US20110016041A1 (en) * | 2009-07-14 | 2011-01-20 | Scragg Ernest M | Triggering Fraud Rules for Financial Transactions |
US20110016052A1 (en) * | 2009-07-16 | 2011-01-20 | Scragg Ernest M | Event Tracking and Velocity Fraud Rules for Financial Transactions |
US8752142B2 (en) | 2009-07-17 | 2014-06-10 | American Express Travel Related Services Company, Inc. | Systems, methods, and computer program products for adapting the security measures of a communication network based on feedback |
US10438181B2 (en) | 2009-07-22 | 2019-10-08 | Visa International Service Association | Authorizing a payment transaction using seasoned data |
US9396465B2 (en) | 2009-07-22 | 2016-07-19 | Visa International Service Association | Apparatus including data bearing medium for reducing fraud in payment transactions using a black list |
US20110022518A1 (en) * | 2009-07-22 | 2011-01-27 | Ayman Hammad | Apparatus including data bearing medium for seasoning a device using data obtained from multiple transaction environments |
US8542921B1 (en) | 2009-07-27 | 2013-09-24 | United Services Automobile Association (Usaa) | Systems and methods for remote deposit of negotiable instrument using brightness correction |
US9779392B1 (en) | 2009-08-19 | 2017-10-03 | United Services Automobile Association (Usaa) | Apparatuses, methods and systems for a publishing and subscribing platform of depositing negotiable instruments |
TWI367452B (en) * | 2009-08-21 | 2012-07-01 | Shih Chin Lee | Method for detecting abnormal transactions of financial assets and information processing device performing the method |
US8977571B1 (en) | 2009-08-21 | 2015-03-10 | United Services Automobile Association (Usaa) | Systems and methods for image monitoring of check during mobile deposit |
US8699779B1 (en) | 2009-08-28 | 2014-04-15 | United Services Automobile Association (Usaa) | Systems and methods for alignment of check during mobile deposit |
US8751375B2 (en) | 2009-08-31 | 2014-06-10 | Bank Of America Corporation | Event processing for detection of suspicious financial activity |
IN2012DN03025A (en) * | 2009-09-09 | 2015-07-31 | Ct Se Llc | |
US8620798B2 (en) * | 2009-09-11 | 2013-12-31 | Visa International Service Association | System and method using predicted consumer behavior to reduce use of transaction risk analysis and transaction denials |
US20110066497A1 (en) * | 2009-09-14 | 2011-03-17 | Choicestream, Inc. | Personalized advertising and recommendation |
US8489415B1 (en) | 2009-09-30 | 2013-07-16 | Mckesson Financial Holdings Limited | Systems and methods for the coordination of benefits in healthcare claim transactions |
US8214285B2 (en) | 2009-10-05 | 2012-07-03 | Cybersource Corporation | Real time adaptive control of transaction review rate score curve |
US8805737B1 (en) * | 2009-11-02 | 2014-08-12 | Sas Institute Inc. | Computer-implemented multiple entity dynamic summarization systems and methods |
US10467687B2 (en) * | 2009-11-25 | 2019-11-05 | Symantec Corporation | Method and system for performing fraud detection for users with infrequent activity |
US8621636B2 (en) | 2009-12-17 | 2013-12-31 | American Express Travel Related Services Company, Inc. | Systems, methods, and computer program products for collecting and reporting sensor data in a communication network |
US9756076B2 (en) | 2009-12-17 | 2017-09-05 | American Express Travel Related Services Company, Inc. | Dynamically reacting policies and protections for securing mobile financial transactions |
US8489499B2 (en) | 2010-01-13 | 2013-07-16 | Corelogic Solutions, Llc | System and method of detecting and assessing multiple types of risks related to mortgage lending |
US8650129B2 (en) * | 2010-01-20 | 2014-02-11 | American Express Travel Related Services Company, Inc. | Dynamically reacting policies and protections for securing mobile financial transaction data in transit |
US8355934B2 (en) * | 2010-01-25 | 2013-01-15 | Hartford Fire Insurance Company | Systems and methods for prospecting business insurance customers |
US8788296B1 (en) | 2010-01-29 | 2014-07-22 | Mckesson Financial Holdings | Systems and methods for providing notifications of availability of generic drugs or products |
US8386276B1 (en) | 2010-02-11 | 2013-02-26 | Mckesson Financial Holdings Limited | Systems and methods for determining prescribing physician activity levels |
US8321243B1 (en) | 2010-02-15 | 2012-11-27 | Mckesson Financial Holdings Limited | Systems and methods for the intelligent coordination of benefits in healthcare transactions |
US8738418B2 (en) * | 2010-03-19 | 2014-05-27 | Visa U.S.A. Inc. | Systems and methods to enhance search data with transaction based data |
US20110231258A1 (en) * | 2010-03-19 | 2011-09-22 | Visa U.S.A. Inc. | Systems and Methods to Distribute Advertisement Opportunities to Merchants |
US20110231224A1 (en) * | 2010-03-19 | 2011-09-22 | Visa U.S.A. Inc. | Systems and Methods to Perform Checkout Funnel Analyses |
US20110231225A1 (en) * | 2010-03-19 | 2011-09-22 | Visa U.S.A. Inc. | Systems and Methods to Identify Customers Based on Spending Patterns |
US9652802B1 (en) | 2010-03-24 | 2017-05-16 | Consumerinfo.Com, Inc. | Indirect monitoring and reporting of a user's credit data |
US8548824B1 (en) | 2010-03-26 | 2013-10-01 | Mckesson Financial Holdings Limited | Systems and methods for notifying of duplicate product prescriptions |
US9171306B1 (en) | 2010-03-29 | 2015-10-27 | Bank Of America Corporation | Risk-based transaction authentication |
US8688468B1 (en) | 2010-03-30 | 2014-04-01 | Mckesson Financial Holdings | Systems and methods for verifying dosages associated with healthcare transactions |
US20130085769A1 (en) * | 2010-03-31 | 2013-04-04 | Risk Management Solutions Llc | Characterizing healthcare provider, claim, beneficiary and healthcare merchant normal behavior using non-parametric statistical outlier detection scoring techniques |
EP2556449A1 (en) | 2010-04-07 | 2013-02-13 | Liveperson Inc. | System and method for dynamically enabling customized web content and applications |
US8676684B2 (en) | 2010-04-12 | 2014-03-18 | Iovation Inc. | System and method for evaluating risk in fraud prevention |
US8725613B1 (en) | 2010-04-27 | 2014-05-13 | Experian Information Solutions, Inc. | Systems and methods for early account score and notification |
US10453093B1 (en) | 2010-04-30 | 2019-10-22 | Lmb Mortgage Services, Inc. | System and method of optimizing matching of leads |
US8473415B2 (en) | 2010-05-04 | 2013-06-25 | Kevin Paul Siegel | System and method for identifying a point of compromise in a payment transaction processing system |
US9129340B1 (en) | 2010-06-08 | 2015-09-08 | United Services Automobile Association (Usaa) | Apparatuses, methods and systems for remote deposit capture with enhanced image detection |
US10360625B2 (en) | 2010-06-22 | 2019-07-23 | American Express Travel Related Services Company, Inc. | Dynamically adaptive policy management for securing mobile financial transactions |
US8850539B2 (en) | 2010-06-22 | 2014-09-30 | American Express Travel Related Services Company, Inc. | Adaptive policies and protections for securing financial transaction data at rest |
US8924296B2 (en) | 2010-06-22 | 2014-12-30 | American Express Travel Related Services Company, Inc. | Dynamic pairing system for securing a trusted communication channel |
JP5321545B2 (en) * | 2010-07-07 | 2013-10-23 | カシオ計算機株式会社 | Terminal device and program |
US9342832B2 (en) * | 2010-08-12 | 2016-05-17 | Visa International Service Association | Securing external systems with account token substitution |
JP5652047B2 (en) * | 2010-08-13 | 2015-01-14 | 富士ゼロックス株式会社 | Information processing apparatus and information processing program |
US9152727B1 (en) | 2010-08-23 | 2015-10-06 | Experian Marketing Solutions, Inc. | Systems and methods for processing consumer information for targeted marketing applications |
US10460377B2 (en) * | 2010-10-20 | 2019-10-29 | Fis Financial Compliance Solutions, Llc | System and method for presenting suspect activity within a timeline |
US8666861B2 (en) | 2010-10-21 | 2014-03-04 | Visa International Service Association | Software and methods for risk and fraud mitigation |
US20120109821A1 (en) * | 2010-10-29 | 2012-05-03 | Jesse Barbour | System, method and computer program product for real-time online transaction risk and fraud analytics and management |
US8930262B1 (en) | 2010-11-02 | 2015-01-06 | Experian Technology Ltd. | Systems and methods of assisted strategy design |
US10977727B1 (en) | 2010-11-18 | 2021-04-13 | AUTO I.D., Inc. | Web-based system and method for providing comprehensive vehicle build information |
US11301922B2 (en) | 2010-11-18 | 2022-04-12 | AUTO I.D., Inc. | System and method for providing comprehensive vehicle information |
US8918465B2 (en) | 2010-12-14 | 2014-12-23 | Liveperson, Inc. | Authentication of service requests initiated from a social networking site |
US9350598B2 (en) | 2010-12-14 | 2016-05-24 | Liveperson, Inc. | Authentication of service requests using a communications initiation feature |
US9058607B2 (en) * | 2010-12-16 | 2015-06-16 | Verizon Patent And Licensing Inc. | Using network security information to detection transaction fraud |
US20120158586A1 (en) * | 2010-12-16 | 2012-06-21 | Verizon Patent And Licensing, Inc. | Aggregating transaction information to detect fraud |
US20120158566A1 (en) * | 2010-12-21 | 2012-06-21 | Corinne Fok | Transaction rate processing apparatuses, methods and systems |
CN102541899B (en) * | 2010-12-23 | 2014-04-16 | 阿里巴巴集团控股有限公司 | Information identification method and equipment |
US20120191468A1 (en) * | 2011-01-21 | 2012-07-26 | Joseph Blue | Apparatuses, Systems, and Methods for Detecting Healthcare Fraud and Abuse |
US20120197802A1 (en) * | 2011-01-28 | 2012-08-02 | Janet Smith | Method and system for determining fraud in a card-not-present transaction |
US9235728B2 (en) | 2011-02-18 | 2016-01-12 | Csidentity Corporation | System and methods for identifying compromised personally identifiable information on the internet |
US8458069B2 (en) * | 2011-03-04 | 2013-06-04 | Brighterion, Inc. | Systems and methods for adaptive identification of sources of fraud |
WO2012127023A1 (en) * | 2011-03-23 | 2012-09-27 | Detica Patent Limited | An automated fraud detection method and system |
US9558519B1 (en) | 2011-04-29 | 2017-01-31 | Consumerinfo.Com, Inc. | Exposing reporting cycle information |
US8548854B2 (en) | 2011-05-10 | 2013-10-01 | Bank Of America Corporation | Content distribution utilizing access parameter data |
US20120290355A1 (en) * | 2011-05-10 | 2012-11-15 | Bank Of America Corporation | Identification of Customer Behavioral Characteristic Data |
TWI560634B (en) * | 2011-05-13 | 2016-12-01 | Univ Nat Taiwan Science Tech | Generating method for transaction modes with indicators for option |
US20120317013A1 (en) * | 2011-06-13 | 2012-12-13 | Ho Ming Luk | Computer-Implemented Systems And Methods For Scoring Stored Enterprise Data |
US20130024358A1 (en) * | 2011-07-21 | 2013-01-24 | Bank Of America Corporation | Filtering transactions to prevent false positive fraud alerts |
US20130024300A1 (en) * | 2011-07-21 | 2013-01-24 | Bank Of America Corporation | Multi-stage filtering for fraud detection using geo-positioning data |
US8571982B2 (en) * | 2011-07-21 | 2013-10-29 | Bank Of America Corporation | Capacity customization for fraud filtering |
US9704195B2 (en) | 2011-08-04 | 2017-07-11 | Fair Isaac Corporation | Multiple funding account payment instrument analytics |
US8732574B2 (en) | 2011-08-25 | 2014-05-20 | Palantir Technologies, Inc. | System and method for parameterizing documents for automatic workflow generation |
US8862767B2 (en) | 2011-09-02 | 2014-10-14 | Ebay Inc. | Secure elements broker (SEB) for application communication channel selector optimization |
US8768866B2 (en) | 2011-10-21 | 2014-07-01 | Sas Institute Inc. | Computer-implemented systems and methods for forecasting and estimation using grid regression |
US11030562B1 (en) | 2011-10-31 | 2021-06-08 | Consumerinfo.Com, Inc. | Pre-data breach monitoring |
BR112014011098A2 (en) * | 2011-11-08 | 2017-05-16 | Vindicia Inc | partial authorization card payment processing that allows partial catches and full deposits |
US10754913B2 (en) | 2011-11-15 | 2020-08-25 | Tapad, Inc. | System and method for analyzing user device information |
US8478688B1 (en) * | 2011-12-19 | 2013-07-02 | Emc Corporation | Rapid transaction processing |
US10380565B1 (en) | 2012-01-05 | 2019-08-13 | United Services Automobile Association (Usaa) | System and method for storefront bank deposits |
US8943002B2 (en) | 2012-02-10 | 2015-01-27 | Liveperson, Inc. | Analytics driven engagement |
US9477988B2 (en) | 2012-02-23 | 2016-10-25 | American Express Travel Related Services Company, Inc. | Systems and methods for identifying financial relationships |
US8538869B1 (en) | 2012-02-23 | 2013-09-17 | American Express Travel Related Services Company, Inc. | Systems and methods for identifying financial relationships |
US8781954B2 (en) | 2012-02-23 | 2014-07-15 | American Express Travel Related Services Company, Inc. | Systems and methods for identifying financial relationships |
US8473410B1 (en) | 2012-02-23 | 2013-06-25 | American Express Travel Related Services Company, Inc. | Systems and methods for identifying financial relationships |
US9633201B1 (en) | 2012-03-01 | 2017-04-25 | The 41St Parameter, Inc. | Methods and systems for fraud containment |
US8805941B2 (en) | 2012-03-06 | 2014-08-12 | Liveperson, Inc. | Occasionally-connected computing interface |
US9521551B2 (en) | 2012-03-22 | 2016-12-13 | The 41St Parameter, Inc. | Methods and systems for persistent cross-application mobile device identification |
US8938462B2 (en) | 2012-04-25 | 2015-01-20 | International Business Machines Corporation | Adaptively assessing object relevance based on dynamic user properties |
US9563336B2 (en) | 2012-04-26 | 2017-02-07 | Liveperson, Inc. | Dynamic user interface customization |
US9400983B1 (en) | 2012-05-10 | 2016-07-26 | Jpmorgan Chase Bank, N.A. | Method and system for implementing behavior isolating prediction model |
US9672196B2 (en) | 2012-05-15 | 2017-06-06 | Liveperson, Inc. | Methods and systems for presenting specialized content using campaign metrics |
US8918891B2 (en) | 2012-06-12 | 2014-12-23 | Id Analytics, Inc. | Identity manipulation detection system and method |
US8856923B1 (en) * | 2012-06-29 | 2014-10-07 | Emc Corporation | Similarity-based fraud detection in adaptive authentication systems |
US8725532B1 (en) | 2012-06-29 | 2014-05-13 | Mckesson Financial Holdings | Systems and methods for monitoring controlled substance distribution |
US10592978B1 (en) * | 2012-06-29 | 2020-03-17 | EMC IP Holding Company LLC | Methods and apparatus for risk-based authentication between two servers on behalf of a user |
US8639619B1 (en) | 2012-07-13 | 2014-01-28 | Scvngr, Inc. | Secure payment method and system |
EP2880619A1 (en) | 2012-08-02 | 2015-06-10 | The 41st Parameter, Inc. | Systems and methods for accessing records via derivative locators |
US9348677B2 (en) | 2012-10-22 | 2016-05-24 | Palantir Technologies Inc. | System and method for batch evaluation programs |
US9953321B2 (en) * | 2012-10-30 | 2018-04-24 | Fair Isaac Corporation | Card fraud detection utilizing real-time identification of merchant test sites |
WO2014078569A1 (en) | 2012-11-14 | 2014-05-22 | The 41St Parameter, Inc. | Systems and methods of global identification |
US10445697B2 (en) | 2012-11-26 | 2019-10-15 | Hartford Fire Insurance Company | System for selection of data records containing structured and unstructured data |
US9576262B2 (en) * | 2012-12-05 | 2017-02-21 | Microsoft Technology Licensing, Llc | Self learning adaptive modeling system |
US10255598B1 (en) | 2012-12-06 | 2019-04-09 | Consumerinfo.Com, Inc. | Credit card account data extraction |
US10049361B2 (en) * | 2012-12-14 | 2018-08-14 | Accenture Global Services Limited | Dynamic authentication technology |
US10552810B1 (en) | 2012-12-19 | 2020-02-04 | United Services Automobile Association (Usaa) | System and method for remote deposit of financial instruments |
CN103209174B (en) * | 2013-03-12 | 2016-03-30 | 华为技术有限公司 | A kind of data prevention method, Apparatus and system |
US9563921B2 (en) * | 2013-03-13 | 2017-02-07 | Opera Solutions U.S.A., Llc | System and method for detecting merchant points of compromise using network analysis and modeling |
US9594907B2 (en) | 2013-03-14 | 2017-03-14 | Sas Institute Inc. | Unauthorized activity detection and classification |
US10140664B2 (en) | 2013-03-14 | 2018-11-27 | Palantir Technologies Inc. | Resolving similar entities from a transaction database |
US9231979B2 (en) | 2013-03-14 | 2016-01-05 | Sas Institute Inc. | Rule optimization for classification and detection |
US9870589B1 (en) | 2013-03-14 | 2018-01-16 | Consumerinfo.Com, Inc. | Credit utilization tracking and reporting |
US8812387B1 (en) | 2013-03-14 | 2014-08-19 | Csidentity Corporation | System and method for identifying related credit inquiries |
US20140279752A1 (en) * | 2013-03-14 | 2014-09-18 | Opera Solutions, Llc | System and Method for Generating Ultimate Reason Codes for Computer Models |
US9633322B1 (en) | 2013-03-15 | 2017-04-25 | Consumerinfo.Com, Inc. | Adjustment of knowledge-based authentication |
US8868486B2 (en) | 2013-03-15 | 2014-10-21 | Palantir Technologies Inc. | Time-sensitive cube |
US20140279102A1 (en) * | 2013-03-15 | 2014-09-18 | Avero Llc | Fraud detection |
US8909656B2 (en) | 2013-03-15 | 2014-12-09 | Palantir Technologies Inc. | Filter chains with associated multipath views for exploring large data sets |
US20140279312A1 (en) * | 2013-03-15 | 2014-09-18 | Capital One Financial Corporation | System and method for providing automated chargeback operations |
US10134040B2 (en) * | 2013-04-26 | 2018-11-20 | Visa International Service Association | Systems and methods for large-scale testing activities discovery |
CN103279868B (en) * | 2013-05-22 | 2016-08-17 | 兰亭集势有限公司 | A kind of method and apparatus of automatic identification swindle order |
US8770478B2 (en) | 2013-07-11 | 2014-07-08 | Scvngr, Inc. | Payment processing with automatic no-touch mode selection |
US9898741B2 (en) * | 2013-07-17 | 2018-02-20 | Visa International Service Association | Real time analytics system |
EP2840542A3 (en) * | 2013-08-19 | 2015-03-25 | Compass Plus (GB) Limited | Method and system for detection of fraudulent transactions |
US10902327B1 (en) | 2013-08-30 | 2021-01-26 | The 41St Parameter, Inc. | System and method for device identification and uniqueness |
US11138578B1 (en) | 2013-09-09 | 2021-10-05 | United Services Automobile Association (Usaa) | Systems and methods for remote deposit of currency |
US8966074B1 (en) * | 2013-09-13 | 2015-02-24 | Network Kinetix, LLC | System and method for real-time analysis of network traffic |
US8938686B1 (en) | 2013-10-03 | 2015-01-20 | Palantir Technologies Inc. | Systems and methods for analyzing performance of an entity |
CN103559208B (en) * | 2013-10-10 | 2017-03-01 | 北京智谷睿拓技术服务有限公司 | The ranking fraud detection method of application program and ranking fraud detection system |
CN103530796B (en) | 2013-10-10 | 2016-06-01 | 北京智谷睿拓技术服务有限公司 | The active period detection method of application program and active period detection system |
US9286514B1 (en) | 2013-10-17 | 2016-03-15 | United Services Automobile Association (Usaa) | Character count determination for a digital image |
US10102536B1 (en) | 2013-11-15 | 2018-10-16 | Experian Information Solutions, Inc. | Micro-geographic aggregation system |
US9799021B1 (en) | 2013-11-26 | 2017-10-24 | Square, Inc. | Tip processing at a point-of-sale system |
US9105000B1 (en) | 2013-12-10 | 2015-08-11 | Palantir Technologies Inc. | Aggregating data from a plurality of data sources |
US9210183B2 (en) * | 2013-12-19 | 2015-12-08 | Microsoft Technology Licensing, Llc | Detecting anomalous activity from accounts of an online service |
US9449344B2 (en) | 2013-12-23 | 2016-09-20 | Sap Se | Dynamically retraining a prediction model based on real time transaction data |
US9647999B2 (en) | 2014-02-07 | 2017-05-09 | Bank Of America Corporation | Authentication level of function bucket based on circumstances |
US9286450B2 (en) | 2014-02-07 | 2016-03-15 | Bank Of America Corporation | Self-selected user access based on specific authentication types |
US9223951B2 (en) | 2014-02-07 | 2015-12-29 | Bank Of America Corporation | User authentication based on other applications |
US9208301B2 (en) | 2014-02-07 | 2015-12-08 | Bank Of America Corporation | Determining user authentication requirements based on the current location of the user in comparison to the users's normal boundary of location |
US9965606B2 (en) | 2014-02-07 | 2018-05-08 | Bank Of America Corporation | Determining user authentication based on user/device interaction |
US10019744B2 (en) | 2014-02-14 | 2018-07-10 | Brighterion, Inc. | Multi-dimensional behavior device ID |
US9779407B2 (en) | 2014-08-08 | 2017-10-03 | Brighterion, Inc. | Healthcare fraud preemption |
US10262362B1 (en) | 2014-02-14 | 2019-04-16 | Experian Information Solutions, Inc. | Automatic generation of code for attributes |
US10002352B2 (en) | 2014-03-04 | 2018-06-19 | Bank Of America Corporation | Digital wallet exposure reduction |
US9721268B2 (en) | 2014-03-04 | 2017-08-01 | Bank Of America Corporation | Providing offers associated with payment credentials authenticated in a specific digital wallet |
US9721248B2 (en) | 2014-03-04 | 2017-08-01 | Bank Of America Corporation | ATM token cash withdrawal |
US9600817B2 (en) | 2014-03-04 | 2017-03-21 | Bank Of America Corporation | Foreign exchange token |
US9424572B2 (en) | 2014-03-04 | 2016-08-23 | Bank Of America Corporation | Online banking digital wallet management |
US9406065B2 (en) | 2014-03-04 | 2016-08-02 | Bank Of America Corporation | Customer token preferences interface |
US9600844B2 (en) | 2014-03-04 | 2017-03-21 | Bank Of America Corporation | Foreign cross-issued token |
US9830597B2 (en) | 2014-03-04 | 2017-11-28 | Bank Of America Corporation | Formation and funding of a shared token |
US20150262184A1 (en) * | 2014-03-12 | 2015-09-17 | Microsoft Corporation | Two stage risk model building and evaluation |
US10430555B1 (en) | 2014-03-13 | 2019-10-01 | Mckesson Corporation | Systems and methods for determining and communicating information to a pharmacy indicating patient eligibility for an intervention service |
US8924429B1 (en) | 2014-03-18 | 2014-12-30 | Palantir Technologies Inc. | Determining and extracting changed data from a data source |
US9836580B2 (en) | 2014-03-21 | 2017-12-05 | Palantir Technologies Inc. | Provider portal |
US11373189B2 (en) | 2014-03-27 | 2022-06-28 | EMC IP Holding Company LLC | Self-learning online multi-layer method for unsupervised risk assessment |
US10297344B1 (en) | 2014-03-31 | 2019-05-21 | Mckesson Corporation | Systems and methods for establishing an individual's longitudinal medication history |
US11386442B2 (en) | 2014-03-31 | 2022-07-12 | Liveperson, Inc. | Online behavioral predictor |
US20180053114A1 (en) | 2014-10-23 | 2018-02-22 | Brighterion, Inc. | Artificial intelligence for context classifier |
US10896421B2 (en) | 2014-04-02 | 2021-01-19 | Brighterion, Inc. | Smart retail analytics and commercial messaging |
US9576030B1 (en) | 2014-05-07 | 2017-02-21 | Consumerinfo.Com, Inc. | Keeping up with the joneses |
US20150324702A1 (en) * | 2014-05-09 | 2015-11-12 | Wal-Mart Stores, Inc. | Predictive pattern profile process |
US9626679B2 (en) | 2014-05-30 | 2017-04-18 | Square, Inc. | Automated fraud detection for point-of-sale devices |
US9996837B2 (en) * | 2014-06-06 | 2018-06-12 | Visa International Service Association | Integration of secure protocols into a fraud detection system |
WO2015191741A1 (en) * | 2014-06-10 | 2015-12-17 | Board Of Trustees Of The Leland Stanford Junior University Office Of The General Counsel | Systems and methods for conducting relationship dependent online transactions |
US11257117B1 (en) | 2014-06-25 | 2022-02-22 | Experian Information Solutions, Inc. | Mobile device sighting location analytics and profiling system |
CN105335855A (en) * | 2014-08-06 | 2016-02-17 | 阿里巴巴集团控股有限公司 | Transaction risk identification method and apparatus |
US20160055427A1 (en) | 2014-10-15 | 2016-02-25 | Brighterion, Inc. | Method for providing data science, artificial intelligence and machine learning as-a-service |
US20150066771A1 (en) | 2014-08-08 | 2015-03-05 | Brighterion, Inc. | Fast access vectors in real-time behavioral profiling |
US9280661B2 (en) | 2014-08-08 | 2016-03-08 | Brighterion, Inc. | System administrator behavior analysis |
US20150339673A1 (en) | 2014-10-28 | 2015-11-26 | Brighterion, Inc. | Method for detecting merchant data breaches with a computer network server |
US20150032589A1 (en) | 2014-08-08 | 2015-01-29 | Brighterion, Inc. | Artificial intelligence fraud management solution |
US20150046332A1 (en) * | 2014-08-08 | 2015-02-12 | Brighterion, Inc. | Behavior tracking smart agents for artificial intelligence fraud protection and management |
US10614452B2 (en) | 2014-09-16 | 2020-04-07 | Mastercard International Incorporated | Systems and methods for providing risk based decisioning service to a merchant |
US10091312B1 (en) | 2014-10-14 | 2018-10-02 | The 41St Parameter, Inc. | Data structures for intelligently resolving deterministic and probabilistic device identifiers to device profiles and/or groups |
US20160078367A1 (en) | 2014-10-15 | 2016-03-17 | Brighterion, Inc. | Data clean-up method for improving predictive model training |
US10546099B2 (en) | 2014-10-15 | 2020-01-28 | Brighterion, Inc. | Method of personalizing, individualizing, and automating the management of healthcare fraud-waste-abuse to unique individual healthcare providers |
US11080709B2 (en) | 2014-10-15 | 2021-08-03 | Brighterion, Inc. | Method of reducing financial losses in multiple payment channels upon a recognition of fraud first appearing in any one payment channel |
US20160071017A1 (en) | 2014-10-15 | 2016-03-10 | Brighterion, Inc. | Method of operating artificial intelligence machines to improve predictive model training and performance |
US20160063502A1 (en) | 2014-10-15 | 2016-03-03 | Brighterion, Inc. | Method for improving operating profits with better automated decision making with artificial intelligence |
US10642957B1 (en) | 2014-10-21 | 2020-05-05 | Mckesson Corporation | Systems and methods for determining, collecting, and configuring patient intervention screening information from a pharmacy |
US10290001B2 (en) | 2014-10-28 | 2019-05-14 | Brighterion, Inc. | Data breach detection |
US9396472B2 (en) | 2014-10-31 | 2016-07-19 | Facebook, Inc. | Systems and methods for dynamically identifying illegitimate accounts based on rules |
US10339527B1 (en) | 2014-10-31 | 2019-07-02 | Experian Information Solutions, Inc. | System and architecture for electronic fraud detection |
US9959319B2 (en) * | 2014-11-17 | 2018-05-01 | Sap Se | Event driven data staging and data aging algorithms for personalization data in enterprise search |
US10832176B2 (en) | 2014-12-08 | 2020-11-10 | Mastercard International Incorporated | Cardholder travel detection with internet service |
US10580054B2 (en) | 2014-12-18 | 2020-03-03 | Experian Information Solutions, Inc. | System, method, apparatus and medium for simultaneously generating vehicle history reports and preapproved financing options |
US9922071B2 (en) * | 2014-12-19 | 2018-03-20 | International Business Machines Corporation | Isolation anomaly quantification through heuristical pattern detection |
US9910882B2 (en) * | 2014-12-19 | 2018-03-06 | International Business Machines Corporation | Isolation anomaly quantification through heuristical pattern detection |
US10242019B1 (en) | 2014-12-19 | 2019-03-26 | Experian Information Solutions, Inc. | User behavior segmentation using latent topic detection |
US11302426B1 (en) | 2015-01-02 | 2022-04-12 | Palantir Technologies Inc. | Unified data interface and system |
CN107210918B (en) * | 2015-02-17 | 2021-07-27 | 维萨国际服务协会 | Apparatus and method for transaction processing using token and password based on transaction specific information |
US11423404B2 (en) * | 2015-05-13 | 2022-08-23 | Mastercard International Incorporated | System and methods for enhanced approval of a payment transaction |
US10255561B2 (en) | 2015-05-14 | 2019-04-09 | Mastercard International Incorporated | System, method and apparatus for detecting absent airline itineraries |
US10402790B1 (en) | 2015-05-28 | 2019-09-03 | United Services Automobile Association (Usaa) | Composing a focused document image from multiple image captures or portions of multiple image captures |
US10528948B2 (en) | 2015-05-29 | 2020-01-07 | Fair Isaac Corporation | False positive reduction in abnormality detection system models |
WO2016196806A1 (en) | 2015-06-02 | 2016-12-08 | Liveperson, Inc. | Dynamic communication routing based on consistency weighting and routing rules |
US9727869B1 (en) | 2015-06-05 | 2017-08-08 | Square, Inc. | Expedited point-of-sale merchant payments |
US10628834B1 (en) | 2015-06-16 | 2020-04-21 | Palantir Technologies Inc. | Fraud lead detection system for efficiently processing database-stored data and automatically generating natural language explanatory information of system results for display in interactive user interfaces |
US11151468B1 (en) | 2015-07-02 | 2021-10-19 | Experian Information Solutions, Inc. | Behavior analysis using distributed representations of event data |
US10210520B2 (en) | 2015-07-10 | 2019-02-19 | Mastercard International Incorporated | Co-processing electronic signals for redundancy |
EP3323097A4 (en) * | 2015-07-14 | 2019-02-27 | Mastercard International Incorporated | Analytics rules engine for payment processing system |
US9418337B1 (en) | 2015-07-21 | 2016-08-16 | Palantir Technologies Inc. | Systems and models for data analytics |
US9392008B1 (en) | 2015-07-23 | 2016-07-12 | Palantir Technologies Inc. | Systems and methods for identifying information related to payment card breaches |
US10671915B2 (en) | 2015-07-31 | 2020-06-02 | Brighterion, Inc. | Method for calling for preemptive maintenance and for equipment failure prevention |
CA2994770A1 (en) | 2015-08-05 | 2017-02-09 | Equifax Inc. | Model integration tool |
US20170053281A1 (en) * | 2015-08-20 | 2017-02-23 | Mastercard International Incorporated | Card Continuity System and Method |
US9485265B1 (en) | 2015-08-28 | 2016-11-01 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US9729536B2 (en) | 2015-10-30 | 2017-08-08 | Bank Of America Corporation | Tiered identification federated authentication network system |
US11410230B1 (en) | 2015-11-17 | 2022-08-09 | Consumerinfo.Com, Inc. | Realtime access and control of secure regulated data |
US9767309B1 (en) | 2015-11-23 | 2017-09-19 | Experian Information Solutions, Inc. | Access control system for implementing access restrictions of regulated database records while identifying and providing indicators of regulated database records matching validation criteria |
US10757154B1 (en) | 2015-11-24 | 2020-08-25 | Experian Information Solutions, Inc. | Real-time event-based notification system |
US10489786B2 (en) | 2015-11-24 | 2019-11-26 | Vesta Corporation | Optimization of fraud detection model in real time |
US10496992B2 (en) | 2015-11-24 | 2019-12-03 | Vesta Corporation | Exclusion of nodes from link analysis |
US10628826B2 (en) * | 2015-11-24 | 2020-04-21 | Vesta Corporation | Training and selection of multiple fraud detection models |
US10223429B2 (en) | 2015-12-01 | 2019-03-05 | Palantir Technologies Inc. | Entity data attribution using disparate data sets |
US10504122B2 (en) | 2015-12-17 | 2019-12-10 | Mastercard International Incorporated | Systems and methods for predicting chargebacks |
EP3185184A1 (en) * | 2015-12-21 | 2017-06-28 | Aiton Caldwell SA | The method for analyzing a set of billing data in neural networks |
US10320841B1 (en) * | 2015-12-28 | 2019-06-11 | Amazon Technologies, Inc. | Fraud score heuristic for identifying fradulent requests or sets of requests |
US10097581B1 (en) | 2015-12-28 | 2018-10-09 | Amazon Technologies, Inc. | Honeypot computing services that include simulated computing resources |
US11290486B1 (en) | 2015-12-28 | 2022-03-29 | Amazon Technologies, Inc. | Allocating defective computing resources for honeypot services |
US9792020B1 (en) | 2015-12-30 | 2017-10-17 | Palantir Technologies Inc. | Systems for collecting, aggregating, and storing data, generating interactive user interfaces for analyzing data, and generating alerts based upon collected data |
US9916522B2 (en) * | 2016-03-11 | 2018-03-13 | Kabushiki Kaisha Toshiba | Training constrained deconvolutional networks for road scene semantic segmentation |
US10861019B2 (en) * | 2016-03-18 | 2020-12-08 | Visa International Service Association | Location verification during dynamic data transactions |
US10015185B1 (en) * | 2016-03-24 | 2018-07-03 | EMC IP Holding Company LLC | Risk score aggregation for automated detection of access anomalies in a computer network |
US11334894B1 (en) | 2016-03-25 | 2022-05-17 | State Farm Mutual Automobile Insurance Company | Identifying false positive geolocation-based fraud alerts |
US10163107B1 (en) | 2016-03-31 | 2018-12-25 | Square, Inc. | Technical fallback infrastructure |
US10210518B2 (en) | 2016-04-13 | 2019-02-19 | Abdullah Abdulaziz I. Alnajem | Risk-link authentication for optimizing decisions of multi-factor authentications |
US10460367B2 (en) | 2016-04-29 | 2019-10-29 | Bank Of America Corporation | System for user authentication based on linking a randomly generated number to the user and a physical item |
US10062078B1 (en) * | 2016-06-14 | 2018-08-28 | Square, Inc. | Fraud detection and transaction review |
US10068235B1 (en) * | 2016-06-14 | 2018-09-04 | Square, Inc. | Regulating fraud probability models |
US10409867B1 (en) | 2016-06-16 | 2019-09-10 | Experian Information Solutions, Inc. | Systems and methods of managing a database of alphanumeric values |
US10268635B2 (en) | 2016-06-17 | 2019-04-23 | Bank Of America Corporation | System for data rotation through tokenization |
CN107563757B (en) * | 2016-07-01 | 2020-09-22 | 阿里巴巴集团控股有限公司 | Data risk identification method and device |
US20180025290A1 (en) * | 2016-07-22 | 2018-01-25 | Edwards Lifesciences Corporation | Predictive risk model optimization |
RU2635275C1 (en) * | 2016-07-29 | 2017-11-09 | Акционерное общество "Лаборатория Касперского" | System and method of identifying user's suspicious activity in user's interaction with various banking services |
US11430070B1 (en) | 2017-07-31 | 2022-08-30 | Block, Inc. | Intelligent application of reserves to transactions |
EP3497560B1 (en) | 2016-08-14 | 2022-11-02 | Liveperson, Inc. | Systems and methods for real-time remote control of mobile applications |
US20180060954A1 (en) | 2016-08-24 | 2018-03-01 | Experian Information Solutions, Inc. | Sensors and system for detection of device movement and authentication of device user based on messaging service data from service provider |
US10394871B2 (en) | 2016-10-18 | 2019-08-27 | Hartford Fire Insurance Company | System to predict future performance characteristic for an electronic record |
US10509997B1 (en) * | 2016-10-24 | 2019-12-17 | Mastercard International Incorporated | Neural network learning for the prevention of false positive authorizations |
US9842338B1 (en) | 2016-11-21 | 2017-12-12 | Palantir Technologies Inc. | System to identify vulnerable card readers |
US11250425B1 (en) | 2016-11-30 | 2022-02-15 | Palantir Technologies Inc. | Generating a statistic using electronic transaction data |
US10896422B2 (en) | 2016-12-01 | 2021-01-19 | Mastercard International Incorporated | Systems and methods for detecting collusion between merchants and cardholders |
US9886525B1 (en) | 2016-12-16 | 2018-02-06 | Palantir Technologies Inc. | Data item aggregate probability analysis system |
US10728262B1 (en) | 2016-12-21 | 2020-07-28 | Palantir Technologies Inc. | Context-aware network-based malicious activity warning systems |
US10748154B2 (en) * | 2016-12-23 | 2020-08-18 | Early Warning Services, Llc | System and method using multiple profiles and scores for assessing financial transaction risk |
WO2018124672A1 (en) | 2016-12-28 | 2018-07-05 | Samsung Electronics Co., Ltd. | Apparatus for detecting anomaly and operating method for the same |
US10721262B2 (en) | 2016-12-28 | 2020-07-21 | Palantir Technologies Inc. | Resource-centric network cyber attack warning system |
US11468472B2 (en) | 2017-01-12 | 2022-10-11 | Fair Isaac Corporation | Systems and methods for scalable, adaptive, real-time personalized offers generation |
CN108346048B (en) | 2017-01-23 | 2020-07-28 | 阿里巴巴集团控股有限公司 | Method for adjusting risk parameters, risk identification method and risk identification device |
BR112019015920A8 (en) | 2017-01-31 | 2020-04-28 | Experian Inf Solutions Inc | massive heterogeneous data ingestion and user resolution |
US11625569B2 (en) | 2017-03-23 | 2023-04-11 | Chicago Mercantile Exchange Inc. | Deep learning for credit controls |
US11379732B2 (en) | 2017-03-30 | 2022-07-05 | Deep Detection Llc | Counter fraud system |
US11593773B1 (en) | 2017-03-31 | 2023-02-28 | Block, Inc. | Payment transaction authentication system and method |
US10755281B1 (en) * | 2017-03-31 | 2020-08-25 | Square, Inc. | Payment transaction authentication system and method |
US10650380B1 (en) | 2017-03-31 | 2020-05-12 | Mckesson Corporation | System and method for evaluating requests |
US20180308099A1 (en) * | 2017-04-19 | 2018-10-25 | Bank Of America Corporation | Fraud Detection Tool |
US20180315038A1 (en) | 2017-04-28 | 2018-11-01 | Square, Inc. | Multi-source transaction processing |
CN107423883B (en) * | 2017-06-15 | 2020-04-07 | 创新先进技术有限公司 | Risk identification method and device for to-be-processed service and electronic equipment |
CN109146440B (en) * | 2017-06-16 | 2020-06-05 | 腾讯科技(深圳)有限公司 | Transaction settlement method, device, server and storage medium |
EP3416123A1 (en) | 2017-06-16 | 2018-12-19 | KBC Groep NV | System for identification of fraudulent transactions |
US10313480B2 (en) | 2017-06-22 | 2019-06-04 | Bank Of America Corporation | Data transmission between networked resources |
US10511692B2 (en) | 2017-06-22 | 2019-12-17 | Bank Of America Corporation | Data transmission to a networked resource based on contextual information |
US10524165B2 (en) | 2017-06-22 | 2019-12-31 | Bank Of America Corporation | Dynamic utilization of alternative resources based on token association |
US10915900B1 (en) | 2017-06-26 | 2021-02-09 | Square, Inc. | Interchange action delay based on refund prediction |
US10664528B1 (en) | 2017-06-28 | 2020-05-26 | Wells Fargo Bank, N.A. | Optimizing display of disclosure based on prior interactions |
WO2019013771A1 (en) * | 2017-07-12 | 2019-01-17 | Visa International Service Association | Systems and methods for generating behavior profiles for new entities |
US11216762B1 (en) | 2017-07-13 | 2022-01-04 | Palantir Technologies Inc. | Automated risk visualization using customer-centric data analysis |
US11210276B1 (en) * | 2017-07-14 | 2021-12-28 | Experian Information Solutions, Inc. | Database system for automated event analysis and detection |
US20190026742A1 (en) * | 2017-07-20 | 2019-01-24 | Microsoft Technology Licensing, Llc | Accounting for uncertainty when calculating profit efficiency |
CN107464169B (en) * | 2017-08-10 | 2020-11-10 | 北京星选科技有限公司 | Information output method and device |
US10832250B2 (en) | 2017-08-22 | 2020-11-10 | Microsoft Technology Licensing, Llc | Long-term short-term cascade modeling for fraud detection |
US11276071B2 (en) * | 2017-08-31 | 2022-03-15 | Paypal, Inc. | Unified artificial intelligence model for multiple customer value variable prediction |
US10469504B1 (en) | 2017-09-08 | 2019-11-05 | Stripe, Inc. | Systems and methods for using one or more networks to assess a metric about an entity |
US10552837B2 (en) | 2017-09-21 | 2020-02-04 | Microsoft Technology Licensing, Llc | Hierarchical profiling inputs and self-adaptive fraud detection system |
US10699028B1 (en) | 2017-09-28 | 2020-06-30 | Csidentity Corporation | Identity security architecture systems and methods |
US10896424B2 (en) | 2017-10-26 | 2021-01-19 | Mastercard International Incorporated | Systems and methods for detecting out-of-pattern transactions |
US10896472B1 (en) | 2017-11-14 | 2021-01-19 | Csidentity Corporation | Security and identity verification system and architecture |
US11151573B2 (en) * | 2017-11-30 | 2021-10-19 | Accenture Global Solutions Limited | Intelligent chargeback processing platform |
US11488196B2 (en) * | 2018-01-29 | 2022-11-01 | Mobiry International Inc. | Real-time fully automated incentive-to-needs matching and delivery |
US10692141B2 (en) | 2018-01-30 | 2020-06-23 | PointPredictive Inc. | Multi-layer machine learning classifier with correlative score |
US10956075B2 (en) | 2018-02-02 | 2021-03-23 | Bank Of America Corporation | Blockchain architecture for optimizing system performance and data storage |
US11176101B2 (en) | 2018-02-05 | 2021-11-16 | Bank Of America Corporation | System and method for decentralized regulation and hierarchical control of blockchain architecture |
US10585979B2 (en) | 2018-02-13 | 2020-03-10 | Open Text GXS ULC | Rules/model-based data processing system for intelligent event prediction in an electronic data interchange system |
US11019090B1 (en) * | 2018-02-20 | 2021-05-25 | United Services Automobile Association (Usaa) | Systems and methods for detecting fraudulent requests on client accounts |
US10776462B2 (en) | 2018-03-01 | 2020-09-15 | Bank Of America Corporation | Dynamic hierarchical learning engine matrix |
US10740404B1 (en) | 2018-03-07 | 2020-08-11 | Experian Information Solutions, Inc. | Database system for dynamically generating customized models |
US10805297B2 (en) | 2018-03-09 | 2020-10-13 | Bank Of America Corporation | Dynamic misappropriation decomposition vector assessment |
CN108428132B (en) * | 2018-03-15 | 2020-12-29 | 创新先进技术有限公司 | Fraud transaction identification method, device, server and storage medium |
CN110309840B (en) | 2018-03-27 | 2023-08-11 | 创新先进技术有限公司 | Risk transaction identification method, risk transaction identification device, server and storage medium |
US10877654B1 (en) | 2018-04-03 | 2020-12-29 | Palantir Technologies Inc. | Graphical user interfaces for optimizations |
US11030752B1 (en) | 2018-04-27 | 2021-06-08 | United Services Automobile Association (Usaa) | System, computing device, and method for document detection |
US20190342297A1 (en) | 2018-05-01 | 2019-11-07 | Brighterion, Inc. | Securing internet-of-things with smart-agent technology |
US10754946B1 (en) | 2018-05-08 | 2020-08-25 | Palantir Technologies Inc. | Systems and methods for implementing a machine learning approach to modeling entity behavior |
US11080603B2 (en) * | 2018-05-18 | 2021-08-03 | Google Llc | Systems and methods for debugging neural networks with coverage guided fuzzing |
US20190385170A1 (en) * | 2018-06-19 | 2019-12-19 | American Express Travel Related Services Company, Inc. | Automatically-Updating Fraud Detection System |
US11119630B1 (en) | 2018-06-19 | 2021-09-14 | Palantir Technologies Inc. | Artificial intelligence assisted evaluations and user interface for same |
WO2020046284A1 (en) * | 2018-08-28 | 2020-03-05 | Visa International Service Association | Fuel loyalty rewards |
US10880313B2 (en) | 2018-09-05 | 2020-12-29 | Consumerinfo.Com, Inc. | Database platform for realtime updating of user data from third party sources |
US10778689B2 (en) | 2018-09-06 | 2020-09-15 | International Business Machines Corporation | Suspicious activity detection in computer networks |
ES2880401T3 (en) | 2018-09-25 | 2021-11-24 | Suez Groupe | Computer-implemented method to detect consumers who commit fraud in the consumption of a service |
SG11202103432VA (en) | 2018-10-09 | 2021-05-28 | Visa Int Service Ass | System for designing and validating fine grained event detection rules |
US11321632B2 (en) * | 2018-11-21 | 2022-05-03 | Paypal, Inc. | Machine learning based on post-transaction data |
US10769636B2 (en) | 2018-11-29 | 2020-09-08 | International Business Machines Corporation | Cognitive fraud detection through variance-based network analysis |
US10354205B1 (en) | 2018-11-29 | 2019-07-16 | Capital One Services, Llc | Machine learning system and apparatus for sampling labelled data |
US20210012205A1 (en) * | 2018-12-17 | 2021-01-14 | Sony Corporation | Learning device, identification device, and program |
US11257018B2 (en) * | 2018-12-24 | 2022-02-22 | Hartford Fire Insurance Company | Interactive user interface for insurance claim handlers including identifying insurance claim risks and health scores |
US11151246B2 (en) | 2019-01-08 | 2021-10-19 | EMC IP Holding Company LLC | Risk score generation with dynamic aggregation of indicators of compromise across multiple categories |
US11157835B1 (en) | 2019-01-11 | 2021-10-26 | Experian Information Solutions, Inc. | Systems and methods for generating dynamic models based on trigger events |
US11605085B2 (en) * | 2019-01-24 | 2023-03-14 | Walmart Apollo, Llc | Methods and apparatus for fraud detection |
US11334877B2 (en) * | 2019-02-11 | 2022-05-17 | Bank Of America Corporation | Security tool |
US11748757B1 (en) | 2019-04-19 | 2023-09-05 | Mastercard International Incorporated | Network security systems and methods for detecting fraud |
US11531780B2 (en) | 2019-05-15 | 2022-12-20 | International Business Machines Corporation | Deep learning-based identity fraud detection |
US10657445B1 (en) | 2019-05-16 | 2020-05-19 | Capital One Services, Llc | Systems and methods for training and executing a neural network for collaborative monitoring of resource usage |
US10664742B1 (en) | 2019-05-16 | 2020-05-26 | Capital One Services, Llc | Systems and methods for training and executing a recurrent neural network to determine resolutions |
US11276124B2 (en) * | 2019-07-02 | 2022-03-15 | Sap Se | Machine learning-based techniques for detecting payroll fraud |
US10992765B2 (en) | 2019-08-12 | 2021-04-27 | Bank Of America Corporation | Machine learning based third party entity modeling for preemptive user interactions for predictive exposure alerting |
US11461497B2 (en) | 2019-08-12 | 2022-10-04 | Bank Of America Corporation | Machine learning based third party entity modeling for predictive exposure prevention |
US11250160B2 (en) | 2019-08-12 | 2022-02-15 | Bank Of America Corporation | Machine learning based user and third party entity communications |
US11468272B2 (en) * | 2019-08-15 | 2022-10-11 | Visa International Service Association | Method, system, and computer program product for detecting fraudulent interactions |
US11080352B2 (en) | 2019-09-20 | 2021-08-03 | International Business Machines Corporation | Systems and methods for maintaining data privacy in a shared detection model system |
US11216268B2 (en) | 2019-09-20 | 2022-01-04 | International Business Machines Corporation | Systems and methods for updating detection models and maintaining data privacy |
US11157776B2 (en) | 2019-09-20 | 2021-10-26 | International Business Machines Corporation | Systems and methods for maintaining data privacy in a shared detection model system |
US11188320B2 (en) | 2019-09-20 | 2021-11-30 | International Business Machines Corporation | Systems and methods for updating detection models and maintaining data privacy |
US11580339B2 (en) * | 2019-11-13 | 2023-02-14 | Oracle International Corporation | Artificial intelligence based fraud detection system |
CN114746873A (en) * | 2019-11-20 | 2022-07-12 | 贝宝公司 | Techniques for utilizing post-transaction data of prior transactions to allow use of recent transaction data |
US11947643B2 (en) * | 2019-12-26 | 2024-04-02 | Rakuten Group, Inc. | Fraud detection system, fraud detection method, and program |
US11682041B1 (en) | 2020-01-13 | 2023-06-20 | Experian Marketing Solutions, Llc | Systems and methods of a tracking analytics platform |
US11470143B2 (en) | 2020-01-23 | 2022-10-11 | The Toronto-Dominion Bank | Systems and methods for real-time transfer failure detection and notification |
JP6955287B2 (en) * | 2020-02-01 | 2021-10-27 | Assest株式会社 | Fraudulent Stock Trading Detection Program |
WO2021163333A1 (en) * | 2020-02-12 | 2021-08-19 | Feedzai - Consultadoria E Inovacão Tecnológica, S.A. | Interleaved sequence recurrent neural networks for fraud detection |
JP2021144355A (en) * | 2020-03-10 | 2021-09-24 | Assest株式会社 | Illegal financial transaction detection program |
US11429974B2 (en) * | 2020-07-18 | 2022-08-30 | Sift Science, Inc. | Systems and methods for configuring and implementing a card testing machine learning model in a machine learning-based digital threat mitigation platform |
US11562373B2 (en) * | 2020-08-06 | 2023-01-24 | Accenture Global Solutions Limited | Utilizing machine learning models, predictive analytics, and data mining to identify a vehicle insurance fraud ring |
US11900755B1 (en) | 2020-11-30 | 2024-02-13 | United Services Automobile Association (Usaa) | System, computing device, and method for document detection and deposit processing |
US11605092B2 (en) | 2021-02-16 | 2023-03-14 | Bank Of America Corporation | Systems and methods for expedited resource issue notification and response |
US11636003B2 (en) * | 2021-06-30 | 2023-04-25 | International Business Machines Corporation | Technology for logging levels and transaction log files |
US11961084B1 (en) * | 2021-11-22 | 2024-04-16 | Rsa Security Llc | Machine learning models for fraud detection |
US11886512B2 (en) * | 2022-05-07 | 2024-01-30 | Fair Isaac Corporation | Interpretable feature discovery with grammar-based bayesian optimization |
Family Cites Families (21)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4734564A (en) | 1985-05-02 | 1988-03-29 | Visa International Service Association | Transaction system with off-line risk assessment |
JPH0637177B2 (en) * | 1985-09-27 | 1994-05-18 | カヤバ工業株式会社 | Rear wheel neutral device for front and rear wheel steering vehicles |
JPS6275768A (en) * | 1985-09-30 | 1987-04-07 | Hitachi Ltd | Abnormal transaction detecting system |
JPS63184870A (en) * | 1987-01-28 | 1988-07-30 | Omron Tateisi Electronics Co | Transaction processing device |
US5025372A (en) * | 1987-09-17 | 1991-06-18 | Meridian Enterprises, Inc. | System and method for administration of incentive award program through use of credit |
FR2625343B1 (en) * | 1987-12-29 | 1990-05-04 | Michaud Andre | IMPROVEMENTS TO SIGNAL PROCESSING DEVICES |
FR2651903B1 (en) * | 1989-09-12 | 1991-12-06 | Michaud Andre | METHOD FOR LIMITING THE RISKS ATTACHED TO A COMPUTER TRANSACTION. |
ZA907106B (en) * | 1989-10-06 | 1991-09-25 | Net 1 Products Pty Ltd | Funds transfer system |
US5146067A (en) | 1990-01-12 | 1992-09-08 | Cic Systems, Inc. | Prepayment metering system using encoded purchase cards from multiple locations |
US5058179A (en) | 1990-01-31 | 1991-10-15 | At&T Bell Laboratories | Hierarchical constrained automatic learning network for character recognition |
US5262941A (en) * | 1990-03-30 | 1993-11-16 | Itt Corporation | Expert credit recommendation method and system |
EP0468229A3 (en) * | 1990-07-27 | 1994-01-26 | Hnc Inc | A neural network with expert system functionality |
JP3012297B2 (en) * | 1990-09-03 | 2000-02-21 | 株式会社日立製作所 | Abnormal event identification method and device |
JPH04133243A (en) * | 1990-09-25 | 1992-05-07 | Sony Corp | Manufacture of cathode-ray tube |
US5177342A (en) | 1990-11-09 | 1993-01-05 | Visa International Service Association | Transaction approval system |
US5231570A (en) * | 1990-12-11 | 1993-07-27 | Lee Gerritt S K | Credit verification system |
JPH04220758A (en) * | 1990-12-20 | 1992-08-11 | Fujitsu Ltd | Time sequence data prediction and prediction recognition method |
US5335278A (en) * | 1991-12-31 | 1994-08-02 | Wireless Security, Inc. | Fraud prevention system and process for cellular mobile telephone networks |
US5732397A (en) * | 1992-03-16 | 1998-03-24 | Lincoln National Risk Management, Inc. | Automated decision-making arrangement |
US5416067A (en) * | 1992-07-10 | 1995-05-16 | Pbi - Gordon Corporation | Dry, water-soluble, substituted phenoxy and/or benzoic acid herbicides and method of preparing same |
US5819226A (en) * | 1992-09-08 | 1998-10-06 | Hnc Software Inc. | Fraud detection using predictive modeling |
-
1992
- 1992-09-08 US US07/941,971 patent/US5819226A/en not_active Expired - Lifetime
-
1993
- 1993-09-07 JP JP6507504A patent/JPH08504284A/en active Pending
- 1993-09-07 EP EP93921393A patent/EP0669032B1/en not_active Expired - Lifetime
- 1993-09-07 CA CA002144068A patent/CA2144068A1/en not_active Abandoned
- 1993-09-07 ES ES93921393T patent/ES2108880T3/en not_active Expired - Lifetime
- 1993-09-07 WO PCT/US1993/008400 patent/WO1994006103A1/en active Search and Examination
- 1993-09-07 AU AU48500/93A patent/AU4850093A/en not_active Abandoned
- 1993-09-07 DE DE69315356T patent/DE69315356T2/en not_active Expired - Lifetime
-
1998
- 1998-10-05 US US09/167,102 patent/US6330546B1/en not_active Expired - Lifetime
Also Published As
Publication number | Publication date |
---|---|
EP0669032B1 (en) | 1997-11-19 |
DE69315356D1 (en) | 1998-01-02 |
ES2108880T3 (en) | 1998-01-01 |
EP0669032A1 (en) | 1995-08-30 |
JPH08504284A (en) | 1996-05-07 |
US5819226A (en) | 1998-10-06 |
WO1994006103A1 (en) | 1994-03-17 |
DE69315356T2 (en) | 1998-06-18 |
AU4850093A (en) | 1994-03-29 |
US6330546B1 (en) | 2001-12-11 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
EP0669032B1 (en) | Fraud detection using predictive modelling | |
US8032448B2 (en) | Detecting and measuring risk with predictive models using content mining | |
US6581043B1 (en) | Routing number variable and indexes | |
US6526389B1 (en) | Telecommunications system for generating a three-level customer behavior profile and for detecting deviation from the profile to identify fraud | |
Ghosh et al. | Credit card fraud detection with a neural-network | |
US8458069B2 (en) | Systems and methods for adaptive identification of sources of fraud | |
US5950179A (en) | Method and system for issuing a secured credit card | |
US8600854B2 (en) | Method and system for evaluating customers of a financial institution using customer relationship value tags | |
US6430305B1 (en) | Identity verification methods | |
US7191150B1 (en) | Enhancing delinquent debt collection using statistical models of debt historical information and account events | |
CA2367462C (en) | A system for detecting counterfeit financial card fraud | |
US5918216A (en) | Automatic recognition of periods for financial transactions | |
US20140114821A1 (en) | Apparatus for consolidating financial transaction information | |
EP1358602A2 (en) | Systems and methods for managing accounts | |
Lee et al. | Forecasting creditworthiness: Logistic vs. artificial neural net | |
US20050027667A1 (en) | Method and system for determining whether a situation meets predetermined criteria upon occurrence of an event | |
US20040044604A1 (en) | Method to improved debt collection practices | |
CN113191872A (en) | Non-standard behavior judgment method based on bank flow balance and balance overlap ratio | |
EP0987645A2 (en) | Predicting a future value of a variable associated with an input data sequence | |
US20220138712A1 (en) | Methods and Systems For Rendering Early Access To Paychecks | |
Gür-Ali et al. | Classifying delinquent customers for credit collections: An application of probabilistic inductive learning | |
CN115953023A (en) | Method, device, equipment, medium and product for collecting limit of item attribution party | |
CN117011075A (en) | Financial cashing management system for intelligent payment | |
WO2002037735A2 (en) | System and method for restricting over-limit accounts | |
ÖzDEN | Classifying delinquent customers for credit collections: an application of probabilistic inductive |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
EEER | Examination request | ||
EEER | Examination request |
Effective date: 19970807 |
|
FZDE | Discontinued |
Effective date: 20130328 |