SYSTEM AND METHOD OF DETECTING FRAUD

• US 20100145836A1
• Filed: 02/22/2010
• Published: 06/10/2010
• Est. Priority Date: 10/04/2005
• Status: Abandoned Application

• Current Assignee: CoreLogic Information Solutions Incorporated (CoreLogic Incorporated)
• Original Assignee: BasePoint Analytics LLC (CoreLogic Incorporated)
• Inventors: Baker, James C., Liao, Yuansong, Spoelstra, Jacob
• Application Number:
  US12/710,228
• Publication Number:
  US 20100145836A1
• Time in Patent Office: Days
• US Class Current: 705/30
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 20/04 Payment circuits
• CPC Class Codes: G06Q 20/10 specially adapted for electronic funds transfer [EFT] systems; specially adapted for home banking systems
• CPC Class Codes: G06Q 20/108 Remote banking, e.g. home banking
• 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 20/403 Solvency checks
• CPC Class Codes: G06Q 30/0185 Product, service or business identity fraud
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

Embodiments include systems and methods of detecting fraud. In particular, one embodiment includes a system and method of detecting fraud in transaction data such as payment card transaction data. For example, one embodiment includes a computerized method of detecting that comprises receiving data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity, applying the data to at least one first model, generating a score based on the first model, and generating data indicative of fraud based at least partly on the score. Other embodiments include systems and methods of generating models for use in fraud detection systems.

Claims

1. A computerized method of detecting fraud, the method comprising:

receiving, on at least one processor, data associated with a financial transaction and at least one transacting entity;

storing the data to a data storage;

selecting at least one model from a plurality of models;

applying the data to the selected model;

generating a score based on the selected model; and

generating data indicative of fraud based at least partly on the score.

2. The method of claim 1, wherein the data associated with the transacting entity comprises at least a portion of each of a plurality of historical transactions of the transacting entity.

3. The method of claim 1, further comprising:

authorizing the financial transaction based on the data indicative of fraud.

4. The method of claim 1, wherein the method is performed in real time during authorization of a transaction.

5. The method of claim 1, wherein the method is performed in batch mode after at least one transaction has been completed.

6. The method of claim 1, further comprising:

associating an indicator of fraud with at least one of the transacting entity and an account associated with the transacting entity.

7. The method of claim 1, further comprising:

identifying the transaction data for review based at least in part on the indicator of fraud.

8. The method of claim 1, wherein the selected model comprises a plurality of models.

9. The method of claim 1, further comprising updating the selected model via a model bus which provides for new models to be added and existing models to be removed without the need to retrain or update existing models.

10. The method of claim 9, further comprising selecting another model based on the data associated with the financial transaction.

11. The method of claim 1, wherein the transacting entity is an account, and wherein the method further comprises:

applying the received data to another model;

identifying the transacting entity as being associated with a plurality of clusters, wherein each of the clusters associates with a plurality of accounts based on the application of the received data to the other model and the received data;

identifying a transition associated with the transacting entity between at least two of the clusters; and

generating data indicative of fraud based at least partly on the score and at least partly on the identified transition.

12. The method of claim 1, wherein generating the data indicative of the fraud comprises combining outputs of the plurality of models using at least one of a committee and a panel of experts.

13. The method of claim 1, wherein the selected model comprises at least one of:

a neural network, a cascaded neural network, a support vector machine, a genetic algorithm, a fuzzy logic model, a case-based reasoning model, a decision tree, a naï

ve Bayesian model, a logistic regression model, and a scorecard model.

14. A system for detecting fraud, the system comprising:

a storage configured to receive data associated with at least one transacting entity; and

a processor configured to;

select at least one model;

apply transaction data and the data associated with the at least one transacting entity to at least one model;

generate a score based on the model; and

generate data indicative of fraud based at least partly on the score.

15. The system of claim 14, wherein the processor is further configured to apply transaction data to at least one model, and wherein the system is configured to provide for incorporation of new models or removal of existing models independently of the selected model.

16. The system of claim 15, wherein the selected model is a specialized model.

17. The system of claim 14, further comprising a model expert selector configured to select one or more models for evaluating a particular set of transaction data.

18. The system of claim 17 wherein the model expert selector selects one or more models based on at least two of a trigger, the historical transaction data, data associated with the type or amounts of the transaction, and data indicative of the entities involved.

19. The system of claim 14, wherein the processor is further configured to associate an indicator of fraud with at least one of the transacting entity and an account associated with the transacting entity.

20. The system of claim 14, wherein the processor is further configured to select the at least one model based on the portion of each of the plurality of historical transactions.

21. The system of claim 20, wherein the processor is further configured to select at least another model based on the data associated with the financial transaction.

22. The system of claim 21, wherein the other model comprises a plurality of models.

23. The system of claim 22, wherein the processor is configured to generate the data indicative of the fraud at least in part by combining outputs of the plurality of models using at least one of a committee and a panel of experts.

24. The system of claim 14, wherein the at least one model comprises at least one of:

a neural network, a cascaded neural network, a support vector machine, a genetic algorithm, a fuzzy logic model, a case-based reasoning model, a decision tree, a naï

ve Bayesian model, a logistic regression model, and a scorecard model.

25. The system of claim 14, wherein the processor is further configured to:

apply another model to the portion of each of the plurality of historical transactions; and

identify a first cluster with the transacting entity based on the other model.

26. The system of claim 22, wherein the transacting entity is an account, wherein the processor is further configured to:

apply the received data to another model;

identify the transacting entity as being associated with a plurality of clusters, wherein each of the clusters associates with a plurality of accounts based on the application of the received data to the other model and the received data;

identify a transition associated with the transacting entity between at least two of the clusters; and

generate data indicative of fraud based at least partly on the score and at least partly on the identified transition.

27. A computer readable medium having computer readable program code embodied thereon for detecting fraudulent transactions, the method comprising:

receiving data associated with a financial transaction and at least one transacting entity into a data storage;

applying the data to at least one model;

generating a score based on the model; and

generating data indicative of fraud based at least partly on the score,

wherein the data associated with the transacting entity comprises respective values of at least one data field of each of a plurality of historical transactions of the transacting entity.

28. A computerized fraud detection system, comprising:

means for receiving, on at least one processor, data associated with a financial transaction and at least one transacting entity, wherein the data associated with the transacting entity comprises respective values of at least one data field of each of a plurality of historical transactions of the transacting entity;

means for processing transaction data, said processing means configured to;

apply the data to at least one model;

generate a score based on the model; and

generate data indicative of fraud based at least partly on the score.