Fraud detection and analysis

• US 10,325,271 B2
• Filed: 10/13/2014
• Issued: 06/18/2019
• Est. Priority Date: 06/12/2008
• Status: Active Grant

• Current Assignee: Guardian Analytics Incorporated (Nice Ltd)
• Original Assignee: Guardian Analytics Incorporated (Nice Ltd)
• Inventors: Miltonberger, Tom
• Primary Examiner(s): Chen, Alan
• Application Number:
  US14/512,828
• Publication Number:
  US 20150186901A1
• Time in Patent Office: 1,709 Days
• Field of Search: None
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• 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 30/0185 Product, service or business identity fraud
• CPC Class Codes: G06Q 30/0225 Avoiding frauds
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 50/265 Personal security, identity or safety

Abstract

Systems and methods generate a risk score for an account event. The systems and methods automatically generate a causal model corresponding to a user, wherein the model estimates components of the causal model using event parameters of a previous event undertaken by the user in an account of the user. The systems and methods predict expected behavior of the user during a next event in the account using the causal model. Predicting the expected behavior of the user includes generating expected event parameters of the next event. The systems and methods use a predictive fraud model to generate fraud event parameters. Generation of the fraud event parameters assumes a fraudster is conducting the next event, wherein the fraudster is any person other than the user. The systems and methods generate a risk score of the next event to indicate the relative likelihood the future event is performed by the user.

Claims

1. A method comprising:

generating relationships between event parameters of events and derived parameters corresponding to an account, wherein the events include actions taken in the account during access of the account;

automatically generating and dynamically updating an account model from the relationships;

predicting, during future events with the account model, when a fraudster is perpetuating the future events;

wherein;

predicting when the fraudster is perpetuating the future events comprises predicting expected behavior that includes generating expected event parameters of the future events; and

generating the expected event parameters of the future events includes generating fraud event parameters using a fraud model, wherein;

generating the fraud event parameters assumes a fraudster is conducting the future events;

the fraudster is any person other than an owner of the account, and wherein said fraud model includes a predictive fraud model;

the predictive fraud model is automatically generated by estimating a plurality of fraud components of the predictive fraud model using the fraud event parameters of previous fraudulent events undertaken in a plurality of accounts, wherein the previous fraudulent events are events suspected of having been conducted by the fraudster; and

generating the predictive fraud model includes generating a joint probability distribution that includes the plurality of fraud components, wherein the plurality of fraud components includes a plurality of fraud probability distribution functions that represent the fraud event parameters, wherein the fraud event parameters are observable fraud parameters collected during the previous fraudulent events.

2. The method of claim 1, wherein the relationships include probabilistic relationships.

3. The method of claim 1, wherein the events include previous events and the future events.

4. The method of claim 1, wherein the derived parameters corresponding to the account include derived parameters corresponding to an owner of the account.

5. The method of claim 1, wherein the dynamically updating includes dynamically updating the account model using event parameters of a future event.

6. The method of claim 1, wherein the generating of the account model comprises estimating a plurality of components of the account model using the event parameters of a plurality of previous events undertaken by a user in the account, wherein the plurality of previous events include the previous event.

7. The method of claim 6, wherein the account model includes a causal model corresponding to the user.

8. The method of claim 1, comprising generating a risk score of the future event using information of the account model and the fraud model, wherein the risk score indicates a relative likelihood the future event is performed by the owner.

9. The method of claim 8, wherein the generating of the risk score includes using the expected event parameters and the fraud event parameters, wherein the risk score indicates the relative likelihood the future event is performed by the user versus the fraudster.

10. The method of claim 9, wherein automatically generating the predictive fraud model includes generating statistical relationships between the fraud event parameters and derived fraud parameters, wherein the derived fraud parameters include one or more of a location of the device, identification of the device, and electronic service provider of the device.

11. The method of claim 9, comprising generating the predictive fraud model.

12. The method of claim 11, wherein generating the predictive fraud model comprises generating an original fraud model to include a probability of observing an event given that the event is caused by the fraudster and absent any other information about the event.

13. The method of claim 11, wherein generating the predictive fraud model comprises generating a probabilistic combination of the original fraud model and an impersonation model.

14. The method of claim 13, comprising generating the original fraud model to include a probability of observing an event given that the event is caused by the fraudster and absent any other information about the event.

15. The method of claim 13, wherein generating the predictive fraud model comprises generating the predictive fraud model to include an impersonation probability, wherein the impersonation probability is a probability that the fraudster successfully impersonates a parameter value of an event parameter of a set of events undertaken by the user.

16. The method of claim 13, wherein the impersonation model comprises a probability that the fraudster mimics an event parameter of a set of events undertaken by the user.

17. The method of claim 13, wherein the impersonation model comprises a probability that the fraudster observes an event parameter of a set of events undertaken by the user.

18. The method of claim 13, comprising:

identifying at least one previous fraud event, a previous fraud event comprising a previous event in the account potentially caused by the fraudster;

generating the original fraud model by estimating a plurality of components of the fraud model using event parameters of at least one previous fraud event undertaken in the account, the at least one previous fraud event potentially conducted by the fraudster.

19. The method of claim 18, comprising modifying the predictive fraud model based on at least one previous event potentially conducted by the fraudster.

20. The method of claim 18, comprising generating the predictive fraud model to include a fraud co-occurrence coefficient for at least one previous event potentially conducted by the fraudster.

21. The method of claim 20, wherein the fraud co-occurrence coefficient represents an accumulated mistrust derived recursively from the at least one previous event potentially conducted by the fraudster.

22. The method of claim 20, wherein the fraud co-occurrence coefficient comprises a coefficient representing an affect of a plurality of previous events potentially conducted by the fraudster.

23. The method of claim 9, wherein automatically generating the account model includes generating statistical relationships between components of the plurality of components.

24. The method of claim 9, wherein automatically generating the account model includes generating a joint probability distribution that includes the plurality of components, wherein the plurality of components includes a plurality of probability distribution functions that represent the event parameters of the previous event, wherein the event parameters are observable parameters collected during the previous event.

25. The method of claim 24, wherein the event parameters include one or more of Internet Protocol (IP) data and Hypertext Transfer Protocol (HTTP) data, wherein the IP data includes one or more of an IP address, IP address country, IP address city, IP network block, and internet service provider supporting an event, wherein the HTTP data includes one or more of data of an operating system, a user agent string, a referrer string, and internet browser of a computer used for an event.

26. The method of claim 9, wherein automatically generating the account model includes generating statistical relationships between the event parameters and derived parameters, wherein the derived parameters include one or more of geographic area from which a device is initiating the future event, location of the device, identification of the device, and electronic service provider of the device.

27. The method of claim 9, wherein predicting the expected behavior of the user includes generating expected event parameters of the future event, wherein generating the expected event parameters includes generating a first set of predicted probability distributions that represent the expected event parameters, wherein generating the first set of predicted probability distributions assumes the user is conducting the future event.

28. The method of claim 9, comprising generating an alert corresponding to the future event when the risk score indicates a person other than the user is conducting the future event.

29. The method of claim 9, comprising automatically updating the account model using a second set of event parameters collected during the future event.

30. The method of claim 29, wherein the second set of event parameters is observable parameters collected during the future event.

31. The method of claim 29, wherein automatically updating the account model includes updating a joint probability distribution that includes the plurality of components.

32. The method of claim 29, wherein automatically updating the account model includes updating at least one of the plurality of components.

33. The method of claim 29, wherein automatically updating the account model includes updating at least one of a plurality of probability distribution functions that represent the event parameters, the updating modifying the at least one of the plurality of probability distribution functions by considering data of the second set of event parameters.

34. The method of claim 29, comprising:

generating a probability distribution function for each of the event parameters of the previous event;

generating an updated probability distribution function for each of the event parameters by applying data of a second set of event parameters of the future event to the probability distribution function;

receiving a baseline account model that corresponds to the user, the baseline account model generated without using data of any event; and

generating the account model by generating a joint probability distribution that includes the plurality of components, wherein the plurality of components includes the updated probability distribution function for any event parameter represented in the account model.

35. The method of claim 9, wherein the previous event and the future event comprise at least one of online events, offline events, and multiple channel events, wherein online events are events undertaken via electronic access to the account.

36. The method of claim 9, wherein an event comprises a login event or an activity event.

37. The method of claim 9, comprising:

determining probabilistically that the future event was conducted by the user;

automatically updating the account model using a second set of event parameters collected during the future event.

38. The method of claim 37, comprising updating the account model to include a trust factor, the trust factor representing a probability that the future event was in fact conducted by the user.

39. The method of claim 37, comprising updating the account model to include an accumulated trust factor, the accumulated trust factor representing a cumulative probability across a plurality of events that an event parameter in the plurality of events was in fact conducted by the user.

40. The method of claim 9, wherein automatically generating the account model comprises generating the account model to include a decay parameter, wherein the decay parameter comprises an exponential decay function by which a relative weight of each event in the account changes with passage of time since the event.

41. The method of claim 1, wherein automatically generating the predictive fraud model includes generating statistical relationships between fraud components of the plurality of fraud components.