Method and apparatus for rating asset-backed securities

• US 7,610,243 B2
• Filed: 06/30/2005
• Issued: 10/27/2009
• 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.
• Primary Examiner(s): CHEUNG, MARY DA ZHI WANG
• Assistant Examiner(s): Fertig; Brian
• Application Number:
  US11/169,589
• Publication Number:
  US 20060242047A1
• Time in Patent Office: 1,580 Days
• Field of Search: 705/10, 705 35- 45, 283/44, 283/57, 283/117, 708/105, 708/131, 708/132, 708/134
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• 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 consumer'"'"'s 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 likelihood of default determined by the SOW model, when applied to a loan portfolio, can reduce the amount of credit enhancement required for an asset-backed securities rating.

Claims

1. A method of reducing a cost of borrowing of a lender when the lender issues asset-backed securities (ABS) backed by receivables from loans to borrowers, comprising:

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

(b) electronically estimating a spend capacity of each borrower of a loan based on tradeline data of the borrower, balance transfers of the borrower, and the model of consumer spending patterns;

(c) for each borrower, using the spend capacity as a factor in determining loan amounts to reduce a risk of default; and

(d) applying for an ABS rating in a capital market based on the reduced risk of default of each borrower.

2. The method of claim 1, further comprising:

(e) reducing a level of credit enhancement needed to obtain a particular ABS rating.

3. The method of claim 1, further comprising:

(e) obtaining a higher ABS rating for a particular level of credit enhancement than would be available without the reduced risk of default.

4. The method of claim 1, wherein at least one of the loans is a home equity loan.

5. The method of claim 1, wherein at least one of the loans is an automobile loan.

6. The method of claim 1, further comprising:

(e) increasing the ABS rating based on a capability to anticipate default by a borrower and take preventative measures prior to default.

7. The method of claim 1, further comprising:

(e) determining a risk of prepayment by each borrower.

8. The method of claim 7, further comprising:

(f) obtaining a higher ABS rating for a particular level of credit enhancement than would be available without determining the risk of prepayment.

9. The method of claim 1, further comprising:

(e) increasing an anticipated recovery rate of a defaulted loan based on a capability to anticipate default of a loan prior to a time of default.

10. The method of claim 9, further comprising:

(f) obtaining a higher ABS rating for a particular level of credit enhancement than would be available without the increased anticipated recovery rate.

11. An apparatus for managing asset-backed securities (ABS) based on loans to borrowers, 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 spend panel data;

estimate a spend capacity of each borrower of a loan based on tradeline data of the borrower, balance transfers of the borrower, and the model of consumer spending patterns;

for each borrower, use the spend capacity as a factor in determining loan amounts to reduce a risk of default; and

output the reduced risk of default, wherein the output is used to obtain an ABS rating.

12. The apparatus of claim 11, wherein the reduced risk of default allows reduction of a level of credit enhancement needed to obtain a particular ABS rating.

13. The apparatus of claim 11, wherein the reduced risk of default allows reduction of insurance needed to obtain a particular ABS rating.

14. The apparatus of claim 11, wherein at least one of the loans is a home equity loan.

15. The apparatus of claim 11, wherein at least one of the loans is a home equity line of credit.

16. The apparatus of claim 11, wherein at least one of the loans is a vehicle loan.

17. The apparatus of claim 11, wherein at least one of the loans is a vehicle lease.

18. The apparatus of claim 11, wherein at least one of the loans is a manufactured housing loan.

19. The apparatus of claim 11, wherein at least one of the loans is an equipment lease.

20. The apparatus of claim 11, wherein at least one of the loans is a recreational vehicle lease.

21. The apparatus of claim 11, wherein at least one of the loans is a recreational vehicle loan.

22. The apparatus of claim 11, wherein the processing instructions further direct the processor to indicate loans in danger of default by a borrower early enough to take preventative measures prior to default.

23. The apparatus of claim 22, wherein the indication by the processor allows a higher ABS rating to be obtained for a particular level of credit enhancement than would otherwise be available if no indication were made.

24. The apparatus of claim 11, wherein the processing instructions further direct the processor to determine a risk of prepayment by each borrower.

25. The apparatus of claim 24, wherein the determination of the risk of prepayment allows a higher ABS rating to be obtained for a particular level of credit enhancement than would otherwise be available if no determination of the risk of prepayment were made.

26. A computer program product for managing asset-backed securities (ABS) based on loans to borrowers, said computer program product having computer program code embodied in computer-readable medium, said computer program code comprising:

first computer readable program code which causes a 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 which causes a computer to estimate a spend capacity of each borrower of a loan based on tradeline data of the borrower, balance transfers of the borrower, and the model of consumer spending patterns;

third computer readable program code which causes a computer to use the spend capacity as a factor in determining loans for which the risk of default is reduced; and

fourth computer readable program code which causes a computer to output the reduced risk of default, wherein the output is used to obtain an ABS rating.

27. The computer program product of claim 26, wherein at least one of the loans is a home equity loan.

28. The computer program product of claim 26, wherein at least one of the loans is a vehicle loan.

29. The computer program product of claim 26, further comprising:

fifth computer readable program code which causes a computer to indicate loans in danger of default by a borrower prior to actual default.

30. The computer program product of claim 27, further comprising:

fifth computer readable program code which causes a computer to determine a risk of prepayment by each borrower.