Method and apparatus for development and use of a credit score based on spend capacity

• US 20060242051A1
• 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 7,814,004 B2
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• 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
• CPC Class Codes: G06Q 40/12 Accounting

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. A SOW score focusing on a consumer'"'"'s spending capability can be used in the same manner as a credit bureau score.

Claims

1. A method of developing a credit score for an individual 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 information 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) assigning a credit score to the individual consumer based on the estimated credit-related information.

2. The method of claim 1, wherein the estimated credit-related information includes at least one of the following data types:

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, 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.

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

4. The method of claim 3, wherein said step (c) comprises:

(i) determining which data types are most indicative of the consumer'"'"'s spending patterns; and

(ii) assigning a credit score to the consumer based on a combination of scores of data types most indicative of the consumer'"'"'s spending patterns.

5. The method of claim 1, wherein the credit score is a numeric score.

6. The method of claim 1, wherein the credit score is indicative of the amount of the consumer'"'"'s spend over a given period of time.

7. The method of claim 1, wherein the credit score is indicative of the consumer'"'"'s spending trend over a given time.

8. A method of developing a consumer credit score, comprising:

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

(b) estimating a spend capacity of an 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) assigning a credit score to the consumer based on the estimated spend capacity of the consumer.

9. The method of claim 8, wherein the credit score is a numeric score.

10. The method of claim 9, wherein the credit score reflects the amount of the consumer'"'"'s spend over a given time period.

11. The method of claim 10, wherein the given time period is at least one of a year a quarter, and a month.

12. The method of claim 9, wherein the credit score is a range of numeric scores.

13. The method of claim 9, wherein the credit score includes an indicator indicating a trend of the consumer'"'"'s spend over a given time.

14. The method of claim 13, wherein the indicator is an exponent.

15. The method of claim 8, wherein the credit score is further based on at least one of the following data types:

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, 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.

16. An apparatus for developing a consumer credit score, 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 information of an individual consumer based on tradeline data of the individual consumer, balance transfers of the individual consumer, and the model of consumer spending patterns; and

assign a credit score to the consumer based on the estimated credit-related information.

17. The apparatus of claim 16, wherein the credit-related information includes a plurality of data types.

18. The apparatus of claim 17, wherein the processing instructions further direct the processor to give an individual score to each data type.

19. The apparatus of claim 17, wherein the processing instructions further direct the processor to output a scorecard including analysis of each data type.

20. The apparatus of claim 18, wherein the instructions to assign a credit score to the consumer include instructions for directing the processor to:

determine which data types are most indicative of the consumer'"'"'s spending patterns; and

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

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

first computer readable program 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 means for causing the computer to estimate credit-related information of an 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 means for causing the computer to assign a credit score to the consumer based on the estimated credit-related information.

22. The computer program product of claim 21, wherein the credit-related information includes a plurality of data types.

23. The computer program product of claim 22, wherein the control logic further comprises fourth computer readable program means for causing the computer to give an individual score to each data type.

24. The computer program product of claim 23, wherein the third computer readable program means include:

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

sixth computer readable program means for causing the computer to assign a credit score to the consumer based on a combination of individual scores of data types most indicative of the consumer'"'"'s spending patterns.

25. The computer program product of claim 22, wherein the control logic further comprises fourth computer readable program means for causing the computer to output a scorecard including analysis of each data type.