Method and System for Identification By A Cardholder of Credit Card Fraud

• US 20110004498A1
• Filed: 07/01/2009
• Published: 01/06/2011
• Est. Priority Date: 07/01/2009
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Readshaw, Neil Ian
• Application Number:
  US12/496,239
• Publication Number:
  US 20110004498A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 20/24 Credit schemes, i.e. "pay after"
• CPC Class Codes: G06Q 20/3255 using mobile network messaging services for payment, e.g. SMS
• CPC Class Codes: G06Q 20/40 Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 20/425 using two different networks, one for transaction and one for security confirmation
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes

Abstract

A method for fraud detection leverages an existing financial institution'"'"'s fraud classification functionality, which produces a first level detection, with a “user-centric” classification functionality, which produces a “second” or more fine-grained detection regarding a potentially fraudulent transaction. After passing through an existing (“institution-centric”) fraud detection technique, a transaction that has been identified as potentially fraudulent is then subject to further analysis and classification at the “user” level, as it is the user is presumed to be the best source of knowledge of the legitimate credit card use. Information about the transaction is shared with the consumer, preferably via one or more near real-time mechanisms, such as SMS, email, or the like. Based on the user'"'"'s response (or lack thereof, as the case may be), one or more business rules in the institution'"'"'s fraud detection system can then take an appropriate action (e.g., no action, reverse the transaction if complete, deny the transaction if in-progress, or the like).

Claims

1. A method of classifying a transaction involving a card associated with an authorized user, comprising:

receiving transaction data associated with the card at a detection machine;

determining whether the transaction data represents a potentially suspect transaction associated with the card;

if the transaction data represents a potentially suspect transaction, issuing a notification to the authorized user that requests confirmation of the potentially suspect transaction;

determining whether a response to the notification has been received from the authorized user, where the response is based on information that is known to the authorized user and is context-specific to the authorized user; and

based on the response, classifying the transaction data.

2. The method as described in claim 1 wherein determining whether the transaction data represents a potentially suspect transaction occurs during the transaction.

3. The method as described in claim 1 wherein determining whether the transaction data represents a potentially suspect transaction occurs following the transaction.

4. The method as described in claim 1 wherein determining whether the transaction data represents a potentially suspect transaction analyzes the transaction data using a first classification module.

5. The method as described in claim 4 wherein the first classification module is an institution-centric classification module.

6. The method as described in claim 1 wherein the notification is issued in real-time, or near real-time.

7. The method as described in claim 1 further including determining whether the response to the notification has been received within a configurable time period.

8. The method as described in claim 7 wherein if the response to the notification has not been received with the configurable time period, classifying the transaction data as representing a suspect transaction.

9. The method as described in claim 1 wherein the card is a credit card.

10. The method as described in claim 1 wherein the notification is issued via one of:

a text message, an email, and a web application.

11. The method as described in claim 10 wherein the notification includes an alias for an account number.

12. The method as described in claim 11 wherein the notification includes a date and time of the transaction, an amount of the transaction, a merchant identifier, and the alias.

13. The method as described in claim 1 wherein the response to the notification is one of:

a valid transaction, a fraudulent transaction, an uncertain transaction, and no response.

14. A data processing system, comprising:

a processor;

computer memory holding computer instructions for carrying out a fraud detection method comprising;

receiving transaction data associated with a credit card;

determining whether the transaction data represents a potentially suspect transaction associated with the credit card;

if the transaction data represents a potentially suspect transaction, issuing a notification to an authorized user that requests confirmation of the potentially suspect transaction;

determining whether a response to the notification has been received from the authorized user, where the response is based on information that is known to the authorized user and is context-specific to the authorized user; and

based on the response, classifying the transaction data.

15. The data processing system as described in claim 14 wherein determining whether the transaction data represents a potentially suspect transaction occurs during the transaction or after the transaction.

16. The data processing system as described in claim 14 wherein transaction data represents a potentially suspect transaction analyzes the transaction data using a first classification module.

17. The data processing system as described in claim 14 wherein the notification is issued in real-time, or near real-time.

18. The data processing system as described in claim 14 wherein the response to the notification is one of:

a valid transaction, a fraudulent transaction, an uncertain transaction, and no response.

19. A computer program product for use in a data processing system comprising a computer-readable medium holding instructions that when executed by a processor direct a fraud detection mechanism to:

receive an indication of a potentially suspect transaction associated with a card;

issuing a notification to the authorized user that requests confirmation of the potentially suspect transaction;

determining whether a response to the notification has been received from the authorized user, where the response is based on information that is known to the authorized user and is context-specific to the authorized user; and

based on the response, classifying the transaction data.

20. The computer program product as described in claim 19 wherein determining whether the transaction data represents a potentially suspect transaction occurs during a card transaction, or after a card transaction.

21. The computer program product as described in claim 19 wherein the notification is issued in real-time, or near real-time.

22. The computer program product as described in claim 19 wherein the response to the notification is one of:

a valid transaction, a fraudulent transaction, an uncertain transaction, and no response.

23. Apparatus for fraud detection, comprising:

a processor;

computer program memory holding computer instructions for directing a fraud detection mechanism to;

receive an indication of a potentially suspect transaction associated with a card;

issuing a notification to the authorized user that requests confirmation of the potentially suspect transaction;

determining whether a response to the notification has been received from the authorized user, where the response is based on information that is known to the authorized user and is context-specific to the authorized user; and

based on the response, classifying the transaction data.