Credit score and scorecard development

• US 8,311,936 B2
• Filed: 11/01/2011
• Issued: 11/13/2012
• Est. Priority Date: 10/29/2004
• Status: Active Grant

• Current Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Original Assignee: American Express Travel Related Services Company, Inc. (American Express Company)
• Inventors: Haggerty, Kathleen, Yuan, Chao M., Okoh, Benedict O., Williamson, Peter L.
• Primary Examiner(s): Holly, John H
• Application Number:
  US13/286,884
• Publication Number:
  US 20120054090A1
• Time in Patent Office: 378 Days
• Field of Search: 705/1.1, 705/38, 705/35, 705/14, 705/26, 705/7, 705/36, 705/40, 455/558, 233/379
• US Class Current: 705/38
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/063112 Skill-based matching of a person or a group to a task
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 20/108 Remote banking, e.g. home banking
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

Share of Wallet (“SOW”) is a modeling approach that utilizes various data sources to provide outputs that describe a consumers spending capability, tradeline history including balance transfers, and balance information. These outputs can be appended to data profiles of customers and prospects and can be utilized to support decisions involving prospecting, new applicant evaluation, and customer management across the lifecycle. The outputs can be used as attributes to consider in developing a credit bureau scorecard.

Claims

1. A method of developing a credit score for an individual consumer, comprising:

estimating, by a computer-based system for developing the credit score for a consumer, credit-related data of the individual consumer using an amount of identified balance transfers of the individual consumer and a model of consumer spending patterns, wherein the credit-related data comprises a spend amount associated with the individual consumer, and wherein the computer-based system comprises a processor and a tangible, non-transitory memory;

offsetting, by the computer-based system, the identified balance transfers from the estimated credit-related data; and

determining a credit score for the individual consumer by using the estimated credit-related data.

2. The method of claim 1, wherein the estimated credit-related data include a plurality of data types.

3. The method of claim 2, wherein each data type is assigned an individual score.

4. The method of claim 3, wherein said determining comprises:

determining which data types are most indicative of the individual consumer'"'"'s risk level; and

assigning a credit score to the individual consumer based on a combination of scores of the data types most indicative of the individual consumer'"'"'s risk level.

5. The method of claim 4, wherein said determining comprises using statistical regression analysis to determine the risk of the individual consumer over a given time.

6. The method of claim 4, wherein said assigning comprises adding the scores of the data types most indicative of the individual consumer'"'"'s risk level to assign the credit score.

7. The method of claim 3, wherein said determining comprises adding the individual scores of each data type to determine the credit score for the consumer.

8. The method of claim 2, wherein the plurality of data types includes at least one of:

spend capacity of the consumer, size of the consumer'"'"'s spending wallet over a particular time period, total number of the consumer'"'"'s revolving cards, the consumer'"'"'s revolving balance, the consumer'"'"'s average pay-down percentage for revolving cards, total number of the consumer'"'"'s transacting cards, the consumer'"'"'s transacting balance, a number of balance transfers transacted by the consumer, total amount of the consumer'"'"'s balance transfers, the consumer'"'"'s maximum revolving balance, the consumer'"'"'s maximum transacting balance, the consumer'"'"'s credit limit, size of the consumer'"'"'s revolving spending, and size of the consumer'"'"'s transacting spending.

9. The method of claim 2, wherein at least one of the data types is analyzed in a scorecard.

10. The method of claim 1, wherein the credit score reflects the consumer'"'"'s total spending patterns.

11. The method of claim 1, wherein the credit score reflects the consumer'"'"'s plastic spending patterns.

12. A system for developing, a credit score for an individual consumer, the system comprising:

a tangible, non-transitory memory communicating with a processor,

the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising;

estimating, by the processor, credit-related data of the individual consumer using an amount of identified balance transfers of the individual consumer and a model of consumer spending patterns, wherein the credit-related data comprises a spend amount associated with the individual consumer;

offsetting, by the processor, the identified balance transfers from the estimated credit-related data; and

determining a credit score for the individual consumer by using the estimated credit-related data.

13. The system of claim 12, wherein the estimated credit-related data include a plurality of data types.

14. The system of claim 13, wherein the processing instructions further direct the processor to assign an individual score to each data type.

15. The system of claim 14, wherein the instructions to assign an individual score to each data type further direct the processor to:

determine which data types are most indicative of the consumer'"'"'s risk level; and

assign a credit score to the consumer based on a combination of individual scores of the data types most indicative of the consumer'"'"'s risk level.

16. The system of claim 13, wherein the processing instructions further direct the processor to output a scorecard including analysis of the plurality of data types.

17. An article of manufacture including a computer readable storage medium having instructions stored thereon that, in response to execution by a computing device, cause the computing device to perform operations comprising:

estimating, by the computing device, credit-related data of an individual consumer using an amount of identified balance transfers of the individual consumer and a model of consumer spending patterns, wherein the credit-related data comprises a spend amount associated with the individual consumer;

offsetting, by the computing device, the identified balance transfers from the estimated credit-related data; and

calculating, by the computing device, a credit score for the consumer by using the estimated credit-related data.

18. The article of manufacture of claim 17, wherein the estimated credit-related data include a plurality of data types.

19. The article of manufacture of claim 18, further comprising assigning an individual score to each data type.

20. The article of manufacture of claim 19, further comprising

determining which data types are most indicative of the consumer'"'"'s risk level;

and

assigning a credit score to the consumer based on a combination of individual scores of the data types most indicative of the consumer'"'"'s risk level.