Reducing risks related to check verification

• US 20080228635A1
• Filed: 10/25/2007
• Published: 09/18/2008
• Est. Priority Date: 10/24/2005
• Status: Abandoned Application

• Current Assignee: Experian Marketing Solutions Incorporated (Experian plc)
• Original Assignee: Experian Marketing Solutions Incorporated (Experian plc)
• Inventors: Kornegay, Adam T., Megdal, Myles G., Granger, Angela
• Application Number:
  US11/977,742
• Publication Number:
  US 20080228635A1
• Time in Patent Office: Days
• US Class Current: 705/38
• 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
• CPC Class Codes: G06Q 40/08 Insurance

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 method of managing a consumer lifecycle of a customer in a credit-related industry, 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 credit-related characteristic of the individual customer based on tradeline data of the individual customer, balance transfers of the individual customer, and the model of consumer spending patterns as applied to the individual customer;

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

(d) making a decision on how to interact with the individual customer based on the determined risk value.

2. The method of claim 1, wherein said step (c) comprises:

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

3. The method of claim 2, wherein said step (d) comprises:

approving a transaction with the customer when the customer is a low-risk customer; and

declining a transaction with the customer when the customer is a high-risk customer.

4. The method of claim 1, wherein said step (c) comprises:

weighting at least one factor relating to a transaction with the customer based on the estimated credit characteristic; and

calculating a risk value based on the weighted factor.

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

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

5. (canceled)

7. An apparatus for managing a consumer lifecycle of a customer in a credit-related industry, 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 a credit-related characteristic of the individual customer based on tradeline data of the individual customer, balance transfers of the individual customer, and the model of consumer spending patterns as applied to the individual customer;

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

make a decision on how to interact with the individual customer 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 decision on how to interact with the individual customer comprise instructions for directing the processor to:

approve a transaction with the customer when the customer is a low-risk customer; and

decline to approve a transaction with the customer 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 a transaction with the customer based on the estimated credit characteristic; and

calculate a risk value based on the weighted factor.

12. The apparatus of claim 7, wherein the credit-related characteristic is at least one of:

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

11. (canceled)

13. A computer program product comprising a computer usable medium having control logic stored therein for causing a computer to manage a consumer lifecycle of a customer in a credit-related industry, 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 a credit-related characteristic of the individual customer based on tradeline data of the individual customer, balance transfers of the individual customer, and the model of consumer spending patterns as applied to the individual customer;

third computer readable program code means for causing the computer to determine a risk value of the individual customer based on the estimated credit-related characteristic; and

fourth computer readable program code means for causing the computer to make a decision on how to interact with the individual customer based on the determined risk value.

14. The computer program product of claim 13, wherein said third computer readable program code means comprises:

fifth computer readable program code means for causing the computer to determine whether the customer is a high-risk customer or a low-risk customer.

15. The computer program product of claim 14, wherein said fourth computer readable program code means comprises:

sixth computer readable program code means for causing the computer to approve a transaction with the customer when the customer is a low-risk customer; and

seventh computer readable program code means for causing the computer to decline to approve a transaction with the customer when the customer is a high-risk customer.

16. The computer program product of claim 13, wherein said third computer readable program code means comprises:

fifth computer readable program code means for causing the computer to weight at least one factor relating to a transaction with the customer based on the estimated credit characteristic; and

sixth computer readable program code means for causing the computer to calculate a risk value based on the weighted factor.

18. The computer program product of claim 13, wherein the credit-related characteristic is at least one of:

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

17. (canceled)