Cross-channel fraud detection

Patent metadata

Field	Value
Publication number	US-2016196615-A1
Application number	US-201514590382-A
Country	US
Kind code	A1
Filing date	Jan 6, 2015
Priority date	Jan 6, 2015
Publication date	Jul 7, 2016
Grant date	—

Abstract

Systems and methods that facilitate detection of cross-channel fraud are discussed. Detection of cross-channel fraud includes analyzing one or more fraud accounts previously subject to fraud. The analyzing includes identifying one or more common patterns of events associated with fraud. Detection of cross-channel fraud also includes determining a cross-channel fraud metric that measures a likelihood of fraud and monitoring a plurality of events associated with a customer. The detection of cross-channel fraud also includes determining a first account fraud probability associated with the customer based at least in part on a comparison between the plurality of events and the one or more common patterns of events. The plurality of events are analyzed in connection with the cross-channel fraud metric to determine an account cross-channel fraud score associated with the customer.

First claim

What is claimed is:

1 . A system, comprising:
a fraud pattern analysis component that analyzes one or more fraud accounts to identify one or more common patterns of events associated with fraud, wherein each of the one or more fraud accounts has previously been subject to fraud; and an observation component that monitors a plurality of events associated with a customer, wherein the fraud pattern analysis component determines a first account fraud probability associated with the customer based at least in part on a comparison between the plurality of events and the one or more common patterns of events.

2 . The system of claim 1 , further comprising a cross-channel fraud metric component that analyzes the one or more fraud accounts and determines a cross-channel fraud metric that measures a likelihood of fraud, wherein the cross-channel fraud metric component analyzes the plurality of events in connection with the cross-channel fraud metric to determine a customer cross-channel fraud score associated with the customer.

3 . The system of claim 2 , further comprising a communication component that transmits at least one of the cross-channel fraud score or the first account fraud probability to an entity associated with the customer.

4 . The system of claim 3 , wherein the communication component transmits the at least one of the cross-channel fraud score or the first account fraud probability based at least in part on one or more entity-selected settings.

5 . The system of claim 3 , wherein the communication component transmits the at least one of the cross-channel fraud score or the first account fraud probability based at least in part on one or more of the first account fraud probability exceeding a first threshold or the customer cross-channel fraud score exceeding a second threshold.

6 . The system of claim 2 , wherein the cross-channel fraud metric component determines one or more fraud cross-channel fraud score trendlines associated with the one or more fraud accounts, wherein the cross-channel fraud metric component determines a customer cross-channel fraud score trendline associated with the customer account, and wherein the cross-channel fraud metric component determines a second account fraud probability based on a comparison between the customer cross-channel fraud score trendline and the one or more fraud cross-channel fraud score trendlines.

7 . The system of claim 2 , wherein the cross-channel fraud metric component employs logistic regression to identify one or more event types associated with fraud, and wherein the cross-channel fraud metric is based at least in part on the identified one or more event types.

8 . The system of claim 7 , wherein each event of the plurality of events is associated with an event type of the identified one or more event types.

9 . The system of claim 1 , wherein each of the one or more common patterns of events comprises an ordering of the common pattern of events, and wherein the comparison between the plurality of events and the one or more common patterns of events comprises a comparison between the orderings of the one or more common patterns of events and an account ordering of the plurality of events.

10 . The system of claim 1 , further comprising a fraud mitigation component that at least one of locks out the customer account or notifies a customer associated with the customer account when one or more of the first account fraud probability exceeds a first threshold or the second account fraud probability exceeds a second threshold.

11 . A method, comprising:
identifying, by a system comprising a processor, one or more fraud accounts, wherein each of the one or more fraud accounts has previously been subject to fraud; analyzing, by the system, the one or more fraud accounts to determine one or more events associated with an increased probability of fraud; determining, by the system, a cross-channel fraud metric based on the determined one or more events; analyzing, by the system, one or more events associated with a customer; and calculating, by the system, a customer cross-channel fraud score based on the cross-channel fraud metric and the analyzed one or more events.

12 . The method of claim 11 , further comprising:
identifying, by the system, one or more common patterns of events associated with the one or more fraud accounts; and comparing, by the system, a pattern of events to the identified one or more common patterns of events to determine a first account fraud probability.

13 . The method of claim 12 , further comprising transmitting, by the system, at least one of the cross-channel fraud score or the first account fraud probability to an entity associated with the customer.

14 . The method of claim 13 , wherein the at least one of the cross-channel fraud score or the first account fraud probability are transmitted based at least in part on one or more entity-selected settings.

15 . The method of claim 13 , wherein the at least one of the cross-channel fraud score or the first account fraud probability are transmitted based at least in part on one or more of the first account fraud probability exceeding a first threshold or the customer cross-channel fraud score exceeding a second threshold.

16 . The method of claim 11 , further comprising
determining, by the system, one or more fraud cross-channel fraud score trendlines associated with the one or more fraud accounts determining, by the system, a customer cross-channel fraud score trendline associated with the customer; an determining, by the system, a second account fraud probability based on a comparison between the customer cross-channel fraud score trendline and the one or more fraud cross-channel fraud score trendlines.

17 . The method of claim 11 , wherein the determining the cross-channel fraud metric comprises employing logistic regression to identify one or more event types associated with fraud, wherein the cross-channel fraud metric is based at least in part on the identified one or more event types.

18 . The method of claim 17 , wherein each event of the plurality of events is associated with an event type of the identified one or more event types.

19 . A system, comprising:
a fraud pattern analysis component that identifies a pattern of events associated with fraud based on a comparison between a set of events associated with a fraud account and another set of events associated with a non-fraud account; an observation component that monitors a plurality of events occurring across channels associated with a customer; a cross-channel fraud metric component that determines in real-time, or near real-time, a cross-channel fraud score for the plurality of events, wherein the fraud pattern analysis component determines a fraud probability for the customer based in part of the cross-channel fraud score; and a fraud mitigation component that implements a fraud mitigation action based on the fraud probability.

20 . The system of claim 19 , further comprising a communication component that conveys to an entity the fraud probability and the fraud mitigation action, wherein the entity has a fiduciary relationship with the customer.

Assignees

• Wells Fargo Bank Na

Inventors

• Yen John
• Norvell Jeremy
• Wang Michelle H

Classifications

• G06Q20/4016

  involving fraud or risk level assessment in transaction processing · CPC title

• G06Q40/12 Primary

  Accounting · CPC title

• H04W12/12

  Detection or prevention of fraud · CPC title

• G06Q20/40

  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 title

• H04L63/1416

  Event detection, e.g. attack signature detection · CPC title