METHOD AND SYSTEM FOR DETECTING FRAUD BASED ON FINANCIAL RECORDS

• US 20090099884A1
• Filed: 10/15/2007
• Published: 04/16/2009
• Est. Priority Date: 10/15/2007
• Status: Abandoned Application

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: MCI Communications Services Incorporated (Verizon Communications Inc.), Verizon Business Network Services, Inc. (Verizon Communications Inc.)
• Inventors: Arch-Espigares, April, Dang, Chau Nguyen, Hoefelmeyer, Ralph Samuel
• Application Number:
  US11/872,490
• Publication Number:
  US 20090099884A1
• Time in Patent Office: Days
• US Class Current: 705/7.38
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

An approach is disclosed for detecting fraud in financial records of a network subscriber. Financial records are received and impersonal data are extracted. Financial record data are processed to conform to a normalized format. Normalized records can be correlated into groups linked to respective sources such as, for example, an individual, a business entity, or a healthcare practitioner. Digits contained in this data are analyzed to determine a pattern that is indicative of fraud. An alert, to the fact that fraud has been detected with respect to an identified plurality of records if such determination has been made, is then generated.

Claims

1. A method comprising:

receiving financial records of a network subscriber;

extracting impersonal data from the financial records; and

analyzing digits of the impersonal data to determine whether a significant event can be identified.

2. A method as recited in claim 1, wherein the step of analyzing comprises detecting a pattern of digits in an identified plurality of the records that are indicative of fraud;

and further comprising;

generating an alert that fraud has been detected with respect to the identified plurality of records.

3. A method as recited in claim 2, wherein the financial records correspond to healthcare data, accounting data, products data, or services data.

4. A method as recited in claim 1, wherein in the step of analyzing comprises processing data in accordance with Benford'"'"'s law.

5. A method as recited in claim 4, further comprising:

normalizing the financial records; and

wherein the step of analyzing further comprises correlating the normalized records into groups linked to respective sources, and the identified plurality of normalized records are common to one of the groups.

6. A method as recited in claim 5, wherein one of the groups is linked to either an individual, a business entity, or a healthcare practitioner.

7. A method as recited in claim 5, further comprising:

accumulating additional records to expand the database;

subsequently repeating the evaluating step for the expanded database;

maintaining a historical database of evaluated events; and

issuing status reports for arbitrary time periods.

8. A method as recited in claim 7, wherein the historical database comprises the number of events evaluated, anomalous events, false positive events, and actual fraudulent events.

9. An apparatus comprising:

a communications interface configured to receive financial records of a subscriber; and

a processor coupled to the communications interface, the processor configured to extract impersonal data from the financial records and to analyze digits of the impersonal data to determine whether a significant event can be identified.

10. An apparatus as recited in claim 9, wherein the processor is further configured to detect a pattern of digits in an identified plurality of the records that are indicative of fraud, and to generate an alert that fraud has been detected with respect to the identified plurality of records.

11. An apparatus as recited in claim 10, wherein the financial records correspond to healthcare data, accounting data, products data, or services data.

12. An apparatus as recited in claim 9, wherein analysis of the digits is performed in accordance with Benford'"'"'s law.

13. An apparatus as recited in claim 12, wherein the processor is further configured to normalize the financial records, and to correlate the normalized records into groups linked to respective sources, the identified plurality of normalized records being common to one of the groups.

14. An apparatus as recited in claim 13, wherein one of the groups is linked to either an individual, a business entity, or a healthcare practitioner.

15. An apparatus as recited in claim 13, further comprising:

a historical database configured to store evaluated events for report generation.

16. An apparatus as recited in claim 15, wherein the historical database comprises the number of events evaluated, anomalous events, false positive events, and actual fraudulent events.

17. A system comprising:

a remote fraud detection unit coupled to a server through a data network, wherein;

the server is configured to process impersonal data of a financial database and to store the processed data in a normalized format; and

the fraud detection unit is configured to detect a pattern of digits in an identified plurality of the records in the stored normalized data that are indicative of fraud.

18. A system as recited in claim 17, wherein the fraud detection unit is configured to process data in accordance with Benford'"'"'s law.

19. A system as recited in claim 17, wherein the financial database comprises healthcare records.

20. A system as recited in claim 17, wherein the identified plurality of records is linked to a common source including one of an individual, a business entity, or a healthcare practitioner.

21. A system as recited in claim 17, wherein the fraud detection unit comprises;

a processor and a rules database;

wherein the processor is configured to process data received from the server in accordance with rules contained in the rules database.

22. A system as recited in claim 21, wherein the fraud detection unit further comprises an historical database containing data representing a number of events evaluated, anomalous events, false positive events, and actual fraudulent events.