REDUCED FRAUD CUSTOMER IMPACT THROUGH PURCHASE PROPENSITY

• US 20140310159A1
• Filed: 04/10/2013
• Published: 10/16/2014
• Est. Priority Date: 04/10/2013
• Status: Abandoned Application

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Zoldi, Scott Michael, Betin, Alexei, Marver, David Frank
• Application Number:
  US13/860,449
• Publication Number:
  US 20140310159A1
• Time in Patent Office: Days
• US Class Current: 705/39
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method, system and computer program product for reduced fraud customer impact through purchase propensity is disclosed. A probability estimate of spending by a consumer in a merchant transaction category is computed based on historical transaction data and consumer profile data, and a propensity score for the merchant transaction is generated. The propensity score represents a propensity for the consumer to conduct the merchant transaction. The propensity score is combined in a fraud model operating in a real-time transaction stream. The fraud score can be adjusted in accordance with the propensity score.

Claims

1. A method comprising:

computing a probability estimate of spending by a consumer in a merchant transaction category based on historical transaction data and consumer profile data;

generating a propensity score for the merchant transaction category based on the probability estimates of spending by the consumer, the propensity score representing a propensity for the consumer to conduct a merchant transaction in a set of spending categories;

combining the propensity score in a fraud model operating in a real-time transaction stream, the fraud model generating a fraud score; and

adjusting the fraud score in accordance with the propensity score, the fraud score representing a relative likelihood that the merchant transaction by the consumer is fraudulent.

2. The method in accordance with claim 1, wherein adjusting the fraud score further comprises reducing the fraud score if the propensity score is high.

3. The method in accordance with claim 2, wherein adjusting the fraud score further comprises increasing the fraud score if the propensity score is low.

4. The method in accordance with claim 1, wherein the merchant transaction data includes merchant category code (MCC) data of a merchant category associated with the customer'"'"'s merchant transaction.

5. The method in accordance with claim 1, wherein the merchant transaction data includes merchant category code (MCC) data of a merchant category not related with the merchant transaction.

6. The method in accordance with claim 1, wherein the consumer profile data includes historical spending data by the consumer.

7. The method in accordance with claim 1, further comprising weighting the propensity score contribution to the fraud model based on a trained model such as logistic regression model.

8. The method in accordance with claim 1, wherein the merchant transaction category is defined by one or more merchant transaction attributes, each of the one or more transaction attributes generating a unique propensity score.

9. The method in accordance with claim 1, further comprising:

segmenting the consumer into each of a plurality of consumer segments, each of the plurality of consumer segments being used to generate a unique propensity score; and

combining the unique propensity scores into a single propensity ratio.

10. A computer program product comprising a machine-readable medium storing instructions that, when executed by at least one programmable processor, cause the at least one programmable processor to perform operations comprising:

computing a probability estimate of spending by a consumer in a merchant transaction category according to merchant transaction data and consumer profile data;

generating a propensity score for a merchant transaction in the merchant transaction category based on the probability estimate of spending by the consumer, the propensity score representing a propensity for the consumer to conduct the merchant transaction;

combining the propensity score in a fraud model operating in a real-time transaction stream, the fraud model generating a fraud score; and

adjusting the fraud score in accordance with the propensity score, the fraud score representing a relative likelihood that the merchant transaction by the consumer is fraudulent.

11. The computer program product in accordance with claim 10, wherein the operation of adjusting the fraud score further comprises reducing the fraud score if the propensity score is high.

12. The computer program product in accordance with claim 11, wherein the operation of adjusting the fraud score further comprises increasing the fraud score if the propensity score is low.

13. The computer program product in accordance with claim 10, wherein the merchant transaction data includes merchant category code (MCC) data of a merchant category associated with the merchant transaction.

14. The computer program product in accordance with claim 10, wherein the merchant transaction data includes merchant category code (MCC) data of a merchant category not related with the merchant transaction.

15. The computer program product in accordance with claim 10, wherein the consumer profile data includes historical spending data by the consumer.

16. The computer program product in accordance with claim 10, further comprising weighting the propensity score in the fraud model based on logistic regression.

17. A system comprising:

at least one programmable processor; and

a machine-readable medium storing instructions that, when executed by the at least one processor, cause the at least one programmable processor to perform operations comprising;

compute a probability estimate of spending by a consumer in a merchant transaction category according to merchant transaction data and consumer profile data;

generate a propensity score for a merchant transaction in the merchant transaction category based on the probability estimate of spending by the consumer, the propensity score representing a propensity for the consumer to conduct the merchant transaction;

combine the propensity in a fraud model operating in a real-time transaction stream, the fraud model generating a fraud score; and

adjust the fraud score in accordance with the propensity score, the fraud score representing a relative likelihood that the merchant transaction by the consumer is fraudulent.

18. The system in accordance with claim 17, wherein the operation of adjusting the fraud score further comprises reducing the fraud score if the propensity score is high.

19. The system in accordance with claim 18, wherein the operation of adjusting the fraud score further comprises increasing the fraud score if the propensity score is low.

20. The system in accordance with claim 17, wherein the merchant transaction data includes merchant category code (MCC) data of a merchant category associated with the merchant transaction.

21. The system in accordance with claim 17, wherein the merchant transaction data includes merchant category code (MCC) data of a merchant category not related with the merchant transaction.

22. The system in accordance with claim 17, wherein the consumer profile data includes historical spending data by the consumer.

23. The system in accordance with claim 17, further comprising weighting the propensity score in the fraud model based on logistic regression.