Reducing risks related to check verification

• US 8,543,499 B2
• Filed: 12/22/2006
• Issued: 09/24/2013
• 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, Okoh, Benedict O., Yuan, Chao M.
• Primary Examiner(s): Holly, John H
• Application Number:
  US11/615,747
• Publication Number:
  US 20070168246A1
• Time in Patent Office: 2,468 Days
• Field of Search: 705/1, 705/40, 705/1.1, 705/35, 705/38, 705/10, 705/7.29, 705/30, 705/36.R, 707/800, 707/736, 715/254
• US Class Current: 705/40
• 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 scores that describe a consumers spending capability, tradeline history including balance transfers, and balance information. Share of wallet scores can be used as a parameter for determining whether or not to accept and/or guarantee a check. The share of wallet can be used to calculate a risk value of a customer. For example, the scores can weight one or more factors related to the check writer and differentiate between a low-risk customer and a high-risk customer.

Claims

1. A computer-based method comprising:

modeling, by a computer-based system for reducing merchant risks comprising a processor and a tangible, non-transitory memory, consumer spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data to produce a model of consumer spending patterns, wherein the computer-based system comprises a processor and a tangible, non-transitory memory, and wherein the model is stored in the memory of the computer-based system;

estimating, by the computer-based system, a credit-related characteristic of the customer based on tradeline data of the customer, identified balance transfers of the customer, and the model of consumer spending patterns

offsetting, by the computer-based system, the previous balance transfers from the

estimated credit-related characteristic;

determining, by the computer-based system, a risk value of the customer based on the estimated credit-related characteristic; and

making a check acceptance decision based on the determined risk value.

2. The method of claim 1, wherein said determining comprises:

determining whether the customer is a high-risk customer or a low-risk customer.

3. The method of claim 2, wherein said making comprises:

accepting the check when the customer is a low-risk customer; and

declining the check when the customer is a high-risk customer.

4. The method of claim 1, wherein said determining comprises:

weighting at least one factor relating to the customer'"'"'s cheek based on the estimated credit characteristic; and

calculating a risk value based on the weighted factor.

5. The method of claim 4, wherein factors relating to the customer'"'"'s check include:

velocity, prior activity, the customer'"'"'s presence in check databases, size of the check, prior had check activity by geographic location, and prior bad check activity by merchant location.

6. The method of claim 1, wherein the credit-related characteristic is at least one of:

a size of the customer'"'"'s spending wallet, a size of the customer'"'"'s revolving spending, a size of the customer'"'"'s transacting spending, a share of wallet size for a particular spend category, a spend capacity of the customer, or an amount of balance transfers transacted by the customer.

7. An apparatus comprising:

a processor for reducing merchant risks; and

a tangible, non-transitory 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 a credit-related characteristic of the customer based on tradeline data of the customer, identified balance transfers of the customer, and the model of consumer spending patterns, wherein the credit-related characteristic comprises a spend amount associated with the individual consumer;

offset the previous balance transfers from the estimated credit-related characteristic;

determine a risk value of the customer based on the estimated credit-related characteristic; and

make a check acceptance decision based on the determined risk value.

8. The apparatus of claim 7, wherein said instructions for directing the processor to determine a risk value comprise instructions for directing the processor to:

determine whether the customer is a high-risk customer or a low-risk customer.

9. The apparatus of claim 8, wherein said instructions for directing the processor to make a check acceptance decision comprise instructions for directing the processor to:

guarantee the check when the customer is a low-risk customer; and

decline to guarantee the check when the customer is a high-risk customer.

10. The apparatus of claim 7, wherein said instructions for directing the processor to determine a risk value comprise instructions for directing the processor to:

weight at least one factor relating to the customer'"'"'s check based on the estimated credit characteristic; and

calculate a risk value based on the weighted factor.

11. The apparatus of claim 10, wherein factors relating to the customer'"'"'s check include:

velocity, prior activity, the customer'"'"'s presence in check databases, size of the check, prior bad check activity by geographic location, and prior bad check activity by merchant location.

12. The apparatus of claim 7, wherein the credit-related characteristic is at least one of a size of the customer'"'"'s spending wallet, a size of the customer'"'"'s revolving spending, a size of the customer'"'"'s transacting spending, a share of wallet size for a particular spend category, a spend capacity of the customer, or an amount of balance transfers transacted by the customer.

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

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

estimating a credit-related characteristic of the customer based on tradeline data of the customer, identified balance transfers of the customer, and the model of consumer spending patterns, wherein the credit-related characteristic comprises a spend amount associated with the individual consumer;

offsetting the previous balance transfers from the estimated credit-related characteristic;

determining a risk value of the customer based on the estimated credit-related characteristic; and

making a check acceptance decision based on the determined risk value.

14. The article of manufacture of claim 13, further comprising determining whether the customer is a high-risk customer or a low-risk customer.

15. The article of manufacture of claim 14, further comprising guaranteeing the check when the customer is a low-risk customer;

and

declining to guarantee the check when the customer is a high-risk customer.

16. The article of manufacture of claim 13, further comprising weighing at least one factor relating to the customer'"'"'s check based on the estimated credit characteristic;

and

calculating a risk value based on the weighted factor.

17. The article of manufacture of claim 16, wherein factors relating to the customer'"'"'s check include:

velocity, prior activity, the customer'"'"'s presence in check databases, size of the check, prior bad check activity by geographic location, and prior bad check activity by merchant location.

18. The article of manufacture of claim 13, wherein the credit-related characteristic is at least one of:

a size of the customer'"'"'s spending wallet, a size of the customer'"'"'s revolving spending, a size of the customer'"'"'s transacting spending, a share of wallet size for a particular spend category, a spend capacity of the customer, or an amount of balance transfers transacted by the customer.