METHOD AND APPARATUS FOR CONSUMER INTERACTION BASED ON SPEND CAPACITY

• US 20110145122A1
• Filed: 02/22/2011
• Published: 06/16/2011
• Est. Priority Date: 10/29/2004
• Status: Abandoned Application

• 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.
• Application Number:
  US13/032,427
• Publication Number:
  US 20110145122A1
• Time in Patent Office: Days
• US Class Current: 705/35
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 20/102 Bill distribution or payments
• 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 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. In addition to credit card companies, SoW outputs may be useful to companies issuing, for example: private label cards, life insurance, on-line brokerages, mutual funds, car sales/leases, hospitals, and home equity lines of credit or loans. “Best customer” models can correlate SoW outputs with various customer groups. A SoW score focusing on a consumer'"'"'s spending capacity can be used in the same manner as a credit bureau score.

Claims

1. A computer-based method of managing a consumer lifecycle in a credit-related industry, comprising:

modeling, by a computer-based system for managing a consumer lifecycle in the credit-related industry comprising a processor and a non-transitory, tangible memory, consumer spending patterns using aggregate consumer data, including tradeline data and consumer panel data to produce a model of consumer spending patterns;

estimating, by the computer-based system, credit-related information of an individual consumer based on a previous balance transfer of the individual consumer and the model of consumer spending patterns, wherein the credit-related information comprises a spend amount associated with the individual consumer;

offsetting, by the computer-based system, the previous balance transfer from the estimated credit-related information;

assigning, by the computer-based system, a credit score to the individual consumer based on the estimated credit-related information, wherein the credit score includes an indicator indicating a trend of spending of the individual consumer over a given time and wherein the indicator is a positive or a negative integer; and

determining a strategy to interact with the individual consumer based on the credit score.

2. The method of claim 1, wherein the credit-related industry is at least one of the following:

a banking industry, a lending industry, a mutual fund industry, a lease and sales industry, a life insurance industry, a brokerage industry, an asset-backed security issuance industry, a loan buyer industry, a credit card industry, and a private label card industry.

3. The method of claim 1, wherein the credit-related industry is at least one of an online retail industry and a mail order industry.

4. The method of claim 1, wherein the credit-related industry is at least one of a gaming industry, a charity fundraising, a university fundraising, communications provider industry, a hospital industry, and a travel industry.

5. The method of claim 1, wherein said determining comprises determining when the individual consumer is nearing default on a loan.

6. The method of claim 5, wherein said determining further comprises determining whether the individual consumer is likely to accept a settlement offer.

7. The method of claim 1, wherein said determining comprises developing a strategy to collect from the individual consumer an amount owed.

8. A system for managing a consumer lifecycle in a credit-related industry, the system comprising:

a non-transitory memory communicating with a processor for managing a consumer lifecycle in a credit-related industry;

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

modeling, by the processor, consumer spending patterns using aggregate consumer data, including tradeline data and consumer panel data to produce a model of consumer spending patterns;

estimating, by the processor, credit-related information of an individual consumer based on a previous balance transfer of the individual consumer and the model of consumer spending patterns, wherein the credit-related information comprises a spend amount associated with the individual consumer;

offsetting, by the processor, the previous balance transfer from the estimated credit-related information;

assigning, by the processor, a credit score to the individual consumer based on the estimated credit-related information, wherein the credit score includes an indicator indicating a trend of spending of the individual consumer over a given time and wherein the indicator is a positive or a negative integer; and

determining, by the processor, a strategy to interact with the individual consumer based on the credit score.

9. The system of claim 8, wherein the credit-related industry is at least one of a banking industry, a lending industry, a mutual fund industry, a lease and sales industry, a life insurance industry, a brokerage industry, an asset-backed security issuance industry, a loan buyer industry, a credit card industry, and a private label card industry.

10. The system of claim 8, wherein the credit-related industry is at least one of a online retail industry and a mail order industry.

11. The system of claim 8, wherein the credit-related industry is at least one of a gaming industry, a charity fundraising, a university fundraising, a communications provider industry, a hospital industry, and a travel industry.

12. The system of claim 8, further comprising determining when the individual consumer is nearing default on a loan.

13. The system of claim 12, further comprising determining whether the individual consumer is likely to accept a settlement offer.

14. The system of claim 12, further comprising developing a strategy to collect from the individual consumer an amount owed.

15. The system of claim 8, further comprising estimating at least one of the following data types:

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

16. An article of manufacture including a non-transitory computer readable medium having instructions stored thereon that, in response to execution by a computing device for managing a consumer lifecycle in a credit-related industry, cause the computing device to perform operations comprising:

modeling, by the computing device, consumer spending patterns using aggregate consumer data, including tradeline data and consumer panel data to produce a model of consumer spending patterns;

estimating, by the computing device, credit-related information of an individual consumer based on a previous balance transfer of the individual consumer and the model of consumer spending patterns, wherein the credit-related information comprises a spend amount associated with the individual consumer;

offsetting, by the computing device, the previous balance transfer from the estimated credit-related information;

assigning, by the computing device, a credit score to the individual consumer based on the estimated credit-related information, wherein the credit score includes an indicator indicating a trend of spending of the individual consumer over a given time and wherein the indicator is a positive or a negative integer; and

determining, by the computing device, a strategy to interact with the individual consumer based on the credit score.

17. The article of manufacture of claim 16, wherein the credit-related industry is at least one of a banking industry, a lending industry, a mutual fund industry, a lease and sales industry, a life insurance industry, a brokerage industry, an asset-backed security issuance industry, a loan buyer industry, a credit card industry, and a private label card industry.

18. The article of manufacture of claim 16, wherein the credit-related industry is at least one a gaming industry, a charity fundraising, a university fundraising, a communications provider industry, a hospital industry, and a travel industry.

19. The article of manufacture of claim 16 further comprising estimating, by the computing device, a spend capacity of the individual consumer solely for the credit-related industry.

20. The article of manufacture of claim 16, further comprising determining, by the computing device, when the individual consumer is nearing default on a loan.