System and method for providing a fraud risk score

• US 20070112667A1
• Filed: 10/30/2006
• Published: 05/17/2007
• Est. Priority Date: 10/31/2005
• Status: Abandoned Application

• Current Assignee: Dun & Bradstreet Corp., DUN & Bradstreet Incorporated (Dun & Bradstreet Corp.), Hoover's Incorporated (Dun & Bradstreet Holdings, Inc.)
• Original Assignee: Dun & Bradstreet Corp.
• Inventors: Rucker, Daniel
• Application Number:
  US11/589,661
• Publication Number:
  US 20070112667A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

A computer-implemented method for providing a predictive measure of fraud risk. The method includes receiving applicant identification data, identifying predictive fraud patterns by matching the applicant identification data to a historical search database, calculating a predictive measure of fraud risk using the predictive fraud patterns; and providing the predictive measure of fraud risk to a user.

Claims

1. A computer-implemented method for providing a predictive measure of fraud risk, the method comprising:

receiving applicant identification data;

identifying predictive fraud patterns by matching the applicant identification data to a historical search database;

calculating a predictive measure of fraud risk using the predictive fraud patterns; and

providing the predictive measure of fraud risk to a user via a report or display.

2. The method of claim 1, wherein the historical search database comprises credit activity data.

3. The method of claim 1, wherein the historical search database is updated in real time.

4. The method of claim 1, wherein the step of identifying predictive fraud patterns comprises analysis of at least one factor selected from the group consisting of:

the industry from which the search was made;

variations in the use of names, addresses and phone numbers within prior searches;

timing of searches made; and

frequency of searches made.

5. The method of claim 1, wherein the step of identifying predictive fraud patterns further comprises matching the applicant identification data to a database of prior business misrepresentations.

6. The method of claim 5, wherein the database of prior business misrepresentations comprises names and addresses of entities that have misrepresented facts to an information provider in the past.

7. The method of claim 1, wherein the step of identifying predictive fraud patterns further comprises matching the applicant identification data to a source of high risk identifiers.

8. The method of claim 7, wherein the high risk identifiers is at least one selected from the group consisting of:

address data facts and telephone data facts.

9. The method of claim 8, wherein the address data facts is at least one selected from the group consisting of:

the type of address provided, the quality of address information provided, and the risk associated with the address location.

10. The method of claim 8, wherein the telephone data facts is at least one selected from the group consisting of:

the type of carrier and line services originally assigned to the number.

11. The method of claim 1, wherein the step of providing the predictive measure of fraud risk to a user comprises providing a numerical indicator indicative of a risk that an applicant may commit a fraud.

12. The method of claim 11, wherein the numerical indicator ranges in value between about 2001 and about 2999.

13. The method of claim 11, further comprising:

breaking up the numerical indicator into a plurality of ranges;

assigning a risk class to each of the plurality of ranges; and

providing an indicator of risk class to a user.

14. The method of claim 11, further comprising providing score reason codes to a user.

15. The method of claim 1, further comprising providing a historical search data match profile to a user.

16. A computer-implemented method for providing a predictive measure of fraud risk, the method comprising:

receiving identification data for an applicant;

identifying predictive fraud patterns by matching the identification data to a historical search database;

matching the identification data to a database of prior business misrepresentations;

matching the identification data to a source of high risk identifiers;

calculating a predictive measure of fraud risk based on at least one selected from the group consisting of;

the predictive fraud patterns, the degree of match between the identification data and the database of prior business representations, and the degree of match between the identification data and the source of high risk identifiers; and

providing the predictive measure of fraud risk to a user via a report or display.

17. A system for providing a fraud risk score comprising:

an interface for receiving applicant identification data;

a first database comprising records of searches for business information in at least one other database;

an evaluator, wherein the evaluator receives the applicant identification data from the interface and calculates a predictive measure of fraud risk by matching the applicant identification data to the records in the first database to identify predictive fraud patterns and produce a fraud risk score;

an output for providing the fraud risk score to a user via a report or display.

18. The system of claim 17, further comprising a second database comprising names and addresses of business entities that have misrepresented facts to database maintainers in the past;

wherein the evaluator matches the applicant identification data to the second database and adjusts the predictive measure of fraud risk according to the degree of match.

19. The system of claim 17, further comprising a third database comprising high risk identifiers;

wherein the evaluator matches the applicant identification data to the third database and adjusts the predictive measure of fraud risk according to the degree of match.

20. The system of claim 17 wherein the fraud risk score is delivered to a user via a computing platform.

21. The system of claim 20 wherein the computing platform is a web-based platform.