Credit score and scorecard development

• US 20060242049A1
• Filed: 06/30/2005
• Published: 10/26/2006
• 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: Williamson, Peter L., Haggerty, Kathleen, Yuan, Chao M., Okoh, Benedict O.
• Granted Patent: US 7,840,484 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• 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 a consumer, comprising:

(a) modeling consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

(b) estimating credit-related data of the individual consumer based on tradeline data of the individual consumer, balance transfers of the individual consumer, and the model of consumer spending patterns; and

(c) using the estimated credit-related data and credit bureau data about the consumer as attributes in determining a credit score for the individual consumer.

2. The method of claim 1, wherein each of the estimated credit-related data and the credit bureau 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 step (c) comprises:

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

(ii) 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 step (i) 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 step (ii) 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 step (c) 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. An apparatus for developing a credit score for a consumer, comprising:

a processor; and

a memory in communication with the processor, wherein the memory stores a plurality of processing instructions for directing the processor to;

model consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

estimate credit-related data of the individual consumer based on tradeline data of the individual consumer, balance transfers of the individual consumer, and the model of consumer spending patterns; and

use the estimated credit-related data and credit bureau data about the consumer as attributes in calculating a credit score for the consumer.

13. The apparatus of claim 12, wherein each of the estimated credit-related data and the credit bureau data include a plurality of data types.

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

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

17. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to develop a credit score for a consumer, the control logic comprising:

first computer readable program code means for causing the computer to model consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

second computer readable program code means for causing the computer to estimate credit-related data of the individual consumer based on tradeline data of the individual consumer, balance transfers of the individual consumer, and the model of consumer spending patterns; and

third computer readable program code means for causing the computer to use the estimated credit-related data and credit bureau data about the consumer as attributes in calculating a credit score for the consumer.

18. The computer program product of claim 17, wherein each of the estimated credit-related data and the credit bureau data include a plurality of data types.

19. The computer program product of claim 18, wherein the control logic further comprises fourth computer readable program means for causing the computer to assign an individual score to each data type.

20. The computer program product of claim 19, wherein the third computer readable program code means includes:

fifth computer readable program code means for causing the computer to determine which data types are most indicative of the consumer'"'"'s risk level; and

sixth computer readable program code means for causing the computer to 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.

21. The computer program product of claim 18, wherein the control logic further comprises fourth computer readable program code means for causing the computer to output a scorecard including analysis of the plurality of data types.