CN103413243A - System and method for analyzing credit card preference - Google Patents
System and method for analyzing credit card preference Download PDFInfo
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- CN103413243A CN103413243A CN2013103445464A CN201310344546A CN103413243A CN 103413243 A CN103413243 A CN 103413243A CN 2013103445464 A CN2013103445464 A CN 2013103445464A CN 201310344546 A CN201310344546 A CN 201310344546A CN 103413243 A CN103413243 A CN 103413243A
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Abstract
The invention discloses a system and method for analyzing credit card preference. The method includes the steps that a data converter has access to a personal credit information basic database and initiates a query request to customer data; the personal credit information basic database returns data to the data converter; the data converter converts the data into data corresponding to and matched with a data processor and sends the data to the data processor; the data processor collects the data and data in an internal database to form data with a single customer as a unit, and the data are sent to a rule engine for preference analysis; the rule engine extracts basic fields from the data to perform preference analysis, and preference analysis is performed on a user through combination of limit preference data and product preference data. The analyzing method is specific to the single user, so that specific service is supplied according to the preference of the user, service differentiation of various users is increased so as to cater to the pleasure of the users, personalized service types are arranged, and therefore the live card rate of credit cards in the financial industry is increased effectively and waste of resources is reduced.
Description
Technical field
The present invention relates to credit card preference analysis system and method.
Background technology
Credit card is used widely in the country of the whole world more than 95%, and by the end of the year 2011, the domestic credit card is sent out total amount and broken through 2.85 hundred million, and credit card has become topmost cashless payment instrument in resident's daily life.Yet in financial industry, credit card field market competition means are very single at present, research for the credit card related data does not all have to go to analyze with naturally artificial angle, most of bank all depends on the credit decision-making unduly and resource drops into, the differentiated service effect is not remarkable, make the integral body of the industry card rate only approximately 50% of living, the frequency of utilization of credit card is far below the frequency of utilization of bank card.Increasingly mature along with market, credit card slowdown in growth, the holder is more and more higher to service request, each credit card issuer also is faced with the challenges such as cost pressure, competition homogeneity, yet there is data research to show, there is the card holder of nearly three one-tenth to think that each row credit card does not have difference, there is the card holder more than fifty percent to think that each bank card has some difference, think and have the card holder of bigger difference only to account for 12% left and right of total number of TB suspects examin ed, therefore how to increase the differentiation of products & services, become the significant problem that competition among banks faces.
Summary of the invention
The object of the invention is to overcome above-mentioned defect, a kind of credit card preference analysis system and method is provided, with differentiation, the increase client viscosity that improves the credit card products & services.
It is of the present invention by card preference analysis system,, described system comprises:
Data converter, be used to accessing the personal credit information basic database, receive customer data, and customer data is changed into to the data with the data processor corresponding interface;
Data processor, the data that send for receiving data converter, simultaneously these data are gathered together with data in internal database, multifile, many recording processing are formed and take single client and be monofile, the infobit data of unit, be sent to the regulation engine for preference analysis;
Regulation engine, it also comprises preference degree analyzing module and type of preferences analysis module, it be take single client and is the data of unit for receiving, and these data are carried out respectively to the analysis of preference degree and type of preferences.
Credit card preference analysis method of the present invention, comprise the steps: data converter access personal credit information basic database, initiates the customer data inquiry request; The personal credit information basic database is to the data converter return data; Data converter receives return data and converts it into the data with the data processor corresponding interface, and is sent to data processor; Data processor receives the data after conversion, simultaneously these data is gathered together with data in internal database, forms to take single client and be the data of unit, and is sent to the regulation engine of preference analysis; Regulation engine receives take single client after the data of unit, also comprises following steps:
According to the data that receive, extract the required basic field of preference analysis: the amount of the highest overdraw credit card; The amount of highest amount credit card; The overdraw of the highest overdraw wallet accounting=the highest overdraw credit card/total overdraw; The overdraw of the highest overdraw card amount utilization rate=the highest overdraw credit card/the highest overdraw credit card amount;
According to described basic field, calculate type of preferences:
If the amount of the amount of the highest overdraw credit card=highest amount credit card is the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<X value is also the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card>y value is the product preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<y value is without obvious preference;
And calculate the preference degree, when the highest overdraw card amount utilization rate<80%:
If the highest overdraw wallet accounting≤A%, the client is poor by the card concentration degree, and the preference driver is poor;
If A%<the highest overdraw wallet accounting≤B%, the client is medium by the card concentration degree, and preference has certain driven nature;
If the highest overdraw wallet accounting>B%, the client concentrates with card, and the preference driven nature is strong.
After the type of preferences that calculates and preference degree are combined, judge:
If it is poor by the card concentration degree to meet amount preference and client, the preference driver is poor, customer credit line preference, and high amount is lower to wallet accounting castering action;
If it is medium by the card concentration degree to meet amount preference and client, preference has certain driven nature, customer credit line preference, and high amount is medium to wallet accounting castering action;
If meet amount preference and client, concentrate with card, the preference driven nature is strong, customer credit line preference, and high amount is larger to wallet accounting castering action;
If it is poor by the card concentration degree to meet product preference and client, the preference driver is poor, client's product preference, and product and service improves lower to wallet accounting castering action;
If it is medium by the card concentration degree to meet product preference and client, preference has certain driven nature, client's product preference, and product and service improves medium to wallet accounting castering action;
If meet product preference and client, concentrate with card, the preference driven nature is strong, client's product preference, and product and service improves larger to wallet accounting castering action.
A kind of credit card preference analysis system, described system comprises:
Data converter, be used to accessing the personal credit information basic database, receive customer data, and customer data is changed into to the data with the data processor corresponding interface;
Data processor, the data that send be used to receiving data converter, gather these data simultaneously together with data in internal database, form to take single client and be the data of unit, is sent to the regulation engine of preference analysis;
Regulation engine, take single client and be the data of unit for receiving, and these data carried out respectively to the analysis of preference degree and type of preferences, and type of preferences and preference degree are combined;
Output unit, for exporting the analysis data of regulation engine.
Credit card preference analysis system and method for the present invention, the data that it returns to the personal credit information basic database and the data of internal database gather, the data of client as unit are take in formation, these data are extracted to the laggard row preference analysis of basic field, comprise amount preference and product preference, by the combination of amount preference and two groups of data of product preference, the user is carried out to preference analysis; Its analytical approach has to unique user, in order to provide targetedly and serve according to this user preference, has increased the differentiation of all types of user service, cater to his tastes, personalized type service is set, effectively increases the card rate alive of financial circles credit card, reduce the wasting of resources.
The accompanying drawing explanation
Fig. 1 is the theory diagram of credit card preference analysis system of the present invention.
Fig. 2 is the theory diagram of regulation engine of the present invention.
Fig. 3 is the process flow diagram of credit card preference analysis method of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the present invention is further illustrated:
As Fig. 1-2, a kind of credit card preference analysis system, described system comprises:
Data converter, for accessing the personal credit information basic database, the personal credit information basic database is the database for the report of stored record personal credit, comprise that the individual is in the credit card of all banks, credit, use card and the refund situation of credit card, the refund of personal loan and violation of agreement, and the request for information of personal credit record, data converter receives customer data, and customer data is changed into to the data with the data processor corresponding interface; Data processor, the data that send for receiving data converter, simultaneously these data are gathered together with data in internal database, internal database record the client I manage it the transaction of credit card, by information such as cards, refund, data processor forms multifile, many recording processing to take single client and be monofile, the infobit data of unit, is sent to the regulation engine for preference analysis; Regulation engine, it also comprises preference degree analyzing module and type of preferences analysis module, it be take single client and is the data of unit for receiving, and these data are carried out respectively to the analysis of preference degree and type of preferences; Output unit, for exporting the analysis data of regulation engine.
Described type of preferences analysis module, specifically for the difference of the amount by the judgement amount of the highest overdraw credit card and highest amount credit card, is judged user's type of preferences.At first extract the required basic field of preference analysis: the amount of the highest overdraw credit card; The amount of highest amount credit card; The overdraw of the highest overdraw wallet accounting=the highest overdraw credit card/total overdraw; The overdraw of the highest overdraw card amount utilization rate=the highest overdraw credit card/the highest overdraw credit card amount.According to basic field, calculate type of preferences:
If the amount of the amount of the highest overdraw credit card=highest amount credit card is the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<X value is also the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card>y value is the product preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<y value is without obvious preference;
The span of X value is: the amount minimum that one's own profession is set≤X value≤highest amount credit card amount * 20%, the span of Y value is: 1000≤Y value≤the highest overdraw overdraft of credit card * 20%.The amount minimum of setting as one's own profession is 2000, and highest amount credit card amount is 100000, and the highest overdraw overdraft of credit card is 100000,2000≤X value≤20000,1000≤Y value≤20000.
The number percent that described preference degree module specifically accounts for total overdraw for the overdraw of the highest overdraw credit card of judgement and the overdraw of the highest overdraw credit card account for the number percent of the highest overdraw credit card amount, judge user's preference degree.According to basic field when the highest overdraw card amount utilization rate<80%: if the highest overdraw wallet accounting≤A%, the client is poor by the card concentration degree, and the preference driver is poor; If A%<the highest overdraw wallet accounting≤B%, the client is medium by the card concentration degree, and preference has certain driven nature; If the highest overdraw wallet accounting>B%, the client concentrates with card, and the preference driven nature is strong.When the highest overdraw card amount utilization rate>80%, the Yin Edu deficiency can't define driver.
As preferably, whole customer credit line utilization rates are carried out from low paramount sequence, A% is the corresponding number percent of sequence the 20th hundredths, B% is the corresponding number percent of sequence the 80th hundredths.
Described regulation engine also comprises the analysis-by-synthesis module, and the data of described analysis-by-synthesis module for preference degree analyzing module and type of preferences analysis module are analyzed, further by two groups of data combinations, analysis-by-synthesis customer priorities.If it is poor by the card concentration degree to meet amount preference and client, the preference driver is poor, customer credit line preference, and high amount is lower to wallet accounting castering action; If it is medium by the card concentration degree to meet amount preference and client, preference has certain driven nature, customer credit line preference, and high amount is medium to wallet accounting castering action; If meet amount preference and client, concentrate with card, the preference driven nature is strong, customer credit line preference, and high amount is larger to wallet accounting castering action; If it is poor by the card concentration degree to meet product preference and client, the preference driver is poor, client's product preference, and product and service improves lower to wallet accounting castering action; If it is medium by the card concentration degree to meet product preference and client, preference has certain driven nature, client's product preference, and product and service improves medium to wallet accounting castering action; If meet product preference and client, concentrate with card, the preference driven nature is strong, client's product preference, and product and service improves larger to wallet accounting castering action.
As Fig. 3, a kind of credit card preference analysis method, comprise the steps:
S301, data converter access personal credit information basic database, initiate the customer data inquiry request;
S302, the personal credit information basic database is to the data converter return data;
S303, data converter receive return data and convert it into the data with the data processor corresponding interface, and are sent to data processor;
S304, data processor receive the data after conversion, simultaneously these data are gathered together with data in internal database, form to take single client and be the data of unit, and are sent to the regulation engine of preference analysis;
S305, regulation engine receive take single client after the data of unit, also comprises following steps:
S306, according to the data that receive, extract the required basic field of preference analysis: the amount of the highest overdraw credit card; The amount of highest amount credit card; The overdraw of the highest overdraw wallet accounting=the highest overdraw credit card/total overdraw; The overdraw of the highest overdraw card amount utilization rate=the highest overdraw credit card/the highest overdraw credit card amount;
S307, according to the basic field in step 1, calculate type of preferences:
If the amount of the amount of the highest overdraw credit card=highest amount credit card is the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<X value is also the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card>y value is the product preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<y value is without obvious preference;
S308, calculate the preference degree, when the highest overdraw card amount utilization rate<80%:
If the highest overdraw wallet accounting≤A%, the client is poor by the card concentration degree, and the preference driver is poor;
If A%<the highest overdraw wallet accounting≤B%, the client is medium by the card concentration degree, and preference has certain driven nature;
If the highest overdraw wallet accounting>B%, the client concentrates with card, and the preference driven nature is strong.
S309, after the type of preferences that calculates and preference degree are combined, judges:
If it is poor by the card concentration degree to meet amount preference and client, the preference driver is poor, customer credit line preference, and high amount is lower to wallet accounting castering action;
If it is medium by the card concentration degree to meet amount preference and client, preference has certain driven nature, customer credit line preference, and high amount is medium to wallet accounting castering action;
If meet amount preference and client, concentrate with card, the preference driven nature is strong, customer credit line preference, and high amount is larger to wallet accounting castering action;
If it is poor by the card concentration degree to meet product preference and client, the preference driver is poor, client's product preference, and product and service improves lower to wallet accounting castering action;
If it is medium by the card concentration degree to meet product preference and client, preference has certain driven nature, client's product preference, and product and service improves medium to wallet accounting castering action;
If meet product preference and client, concentrate with card, the preference driven nature is strong, client's product preference, and product and service improves larger to wallet accounting castering action.
Described analytical approach has to unique user, in order to provide targetedly and serve according to this user preference, has increased the differentiation of all types of user service, cater to his tastes, personalized type service is set, effectively increases the card rate alive of financial circles credit card, reduce the wasting of resources.
Claims (8)
1. a credit card preference analysis system, is characterized in that, described system comprises:
Data converter, be used to accessing the personal credit information basic database, receive customer data, and customer data is changed into to the data with the data processor corresponding interface;
Data processor, the data that send for receiving data converter, simultaneously these data are gathered together with data in internal database, multifile, many recording processing are formed and take single client and be monofile, the infobit data of unit, be sent to the regulation engine for preference analysis;
Regulation engine, it also comprises preference degree analyzing module and type of preferences analysis module, it be take single client and is the data of unit for receiving, and these data are carried out respectively to the analysis of preference degree and type of preferences;
Output unit, for exporting the analysis data of regulation engine.
2. a kind of credit card preference analysis system according to claim 1, it is characterized in that: described type of preferences analysis module, specifically for the difference of the amount by the judgement amount of the highest overdraw credit card and highest amount credit card, is judged user's type of preferences.
3. a kind of credit card preference analysis system according to claim 1, it is characterized in that: the number percent that described preference degree module specifically accounts for total overdraw for the overdraw of the highest overdraw credit card of judgement and the overdraw of the highest overdraw credit card account for the number percent of the highest overdraw credit card amount, judge user's preference degree.
4. a kind of credit card preference analysis system according to claim 1, it is characterized in that: described regulation engine also comprises the analysis-by-synthesis module, the data of described analysis-by-synthesis module for preference degree analyzing module and type of preferences analysis module are analyzed, further by two groups of data combinations, analysis-by-synthesis customer priorities.
5. a credit card preference analysis method, comprise the steps: data converter access personal credit information basic database, initiates the customer data inquiry request; The personal credit information basic database is to the data converter return data; Data converter receives return data and converts it into the data with the data processor corresponding interface, and is sent to data processor; Data processor receives the data after conversion, simultaneously these data is gathered together with data in internal database, forms to take single client and be the data of unit, and is sent to the regulation engine of preference analysis; It is characterized in that: regulation engine receives take single client after the data of unit, also comprises following steps:
According to the data that receive, extract the required basic field of preference analysis: the amount of the highest overdraw credit card; The amount of highest amount credit card; The overdraw of the highest overdraw wallet accounting=the highest overdraw credit card/total overdraw; The overdraw of the highest overdraw card amount utilization rate=the highest overdraw credit card/the highest overdraw credit card amount;
According to described basic field, calculate type of preferences:
If the amount of the amount of the highest overdraw credit card=highest amount credit card is the amount preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<X value is also the amount preference; If the amount of the amount of highest amount credit card-the highest overdraw credit card>y value is the product preference;
If the amount of the amount of highest amount credit card-the highest overdraw credit card<y value is without obvious preference;
And calculate the preference degree, when the highest overdraw card amount utilization rate<80%:
If the highest overdraw wallet accounting≤A%, the client is poor by the card concentration degree, and the preference driver is poor;
If A%<the highest overdraw wallet accounting≤B%, the client is medium by the card concentration degree, and preference has certain driven nature;
If the highest overdraw wallet accounting>B%, the client concentrates with card, and the preference driven nature is strong;
After the type of preferences that calculates and preference degree are combined, judge:
If it is poor by the card concentration degree to meet amount preference and client, the preference driver is poor, customer credit line preference, and high amount is lower to wallet accounting castering action;
If it is medium by the card concentration degree to meet amount preference and client, preference has certain driven nature, customer credit line preference, and high amount is medium to wallet accounting castering action;
If meet amount preference and client, concentrate with card, the preference driven nature is strong, customer credit line preference, and high amount is larger to wallet accounting castering action;
If it is poor by the card concentration degree to meet product preference and client, the preference driver is poor, client's product preference, and product and service improves lower to wallet accounting castering action;
If it is medium by the card concentration degree to meet product preference and client, preference has certain driven nature, client's product preference, and product and service improves medium to wallet accounting castering action;
If meet product preference and client, concentrate with card, the preference driven nature is strong, client's product preference, and product and service improves larger to wallet accounting castering action.
6. a kind of credit card preference analysis method according to claim 5 is characterized in that: described calculating preference degree, when the highest overdraw card amount utilization rate>80%, the Yin Edu deficiency can't define driver.
7. a kind of credit card preference analysis method according to claim 5, is characterized in that, the span of described X value is: the amount minimum that one's own profession is set≤X value≤highest amount credit card amount * 20%.
8. a kind of credit card preference analysis method according to claim 5, is characterized in that, the span of described Y value is: 1000≤Y value≤the highest overdraw overdraft of credit card * 20%.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107833130A (en) * | 2017-10-25 | 2018-03-23 | 中国银行股份有限公司 | A kind of credit card amount lends method and system |
| CN109299397A (en) * | 2018-12-05 | 2019-02-01 | 舒雷 | Credit card vertical search engine |
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| CN101727645A (en) * | 2008-10-23 | 2010-06-09 | 多友科技（北京）有限公司 | Personal credit checking system and method |
| CN101923545A (en) * | 2009-06-15 | 2010-12-22 | 北京百分通联传媒技术有限公司 | Method for recommending personalized information |
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- 2013-08-09 CN CN2013103445464A patent/CN103413243A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030018549A1 (en) * | 2001-06-07 | 2003-01-23 | Huchen Fei | System and method for rapid updating of credit information |
| CN101727645A (en) * | 2008-10-23 | 2010-06-09 | 多友科技（北京）有限公司 | Personal credit checking system and method |
| CN101923545A (en) * | 2009-06-15 | 2010-12-22 | 北京百分通联传媒技术有限公司 | Method for recommending personalized information |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107833130A (en) * | 2017-10-25 | 2018-03-23 | 中国银行股份有限公司 | A kind of credit card amount lends method and system |
| CN109299397A (en) * | 2018-12-05 | 2019-02-01 | 舒雷 | Credit card vertical search engine |
| CN109299397B (en) * | 2018-12-05 | 2021-09-17 | 舒雷 | Credit card vertical search engine |
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Application publication date: 20131127 |