SYSTEMS AND METHODS FOR IDENTIFYING FRAUD IN TRANSACTIONS COMMITTED BY A COHORT OF FRAUDSTERS

• US 20140303993A1
• Filed: 04/03/2014
• Published: 10/09/2014
• Est. Priority Date: 04/08/2013
• Status: Active Application

• Current Assignee: Unisys Corporation
• Original Assignee: Unisys Corporation
• Inventors: Florian, Matthew
• Application Number:
  US14/244,138
• Publication Number:
  US 20140303993A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

Disclosed are systems and methods for identifying potential fraud committed by a cohort of people using models for identifying relationships among people to build the cohort and using fraud models to identify indicators of frauds from attributes of people. Embodiments may predict a likelihood applicants seeking privileges to distribute governmental benefits by identifying members of a cohort associated with an applicant, assigning a value to the strengths of the relationships between people in the cohort, determining weights for identified indicators of fraud identified using fraud models, determining a risk score for the cohort using the values and data points, and then performing a clustering analysis for the risk score of the cohort to determine a risk factor for fraud committed by the applicant and the cohort.

Claims

1. A computer-implemented method for processing applications to provide publicly-funded health benefits, the method comprising:

searching, by the computer, a first database storing one or more prior applicants associated with one or more characteristics;

identifying, by the computer, one or more associates of a new applicant having one or more characteristics of the prior applicants in the first database, wherein an associate is a prior applicant having one or more relationships to the new applicant based upon one or more characteristics common with the new applicant;

identifying, by the computer, one or more indicators of fraud in the first database associated with one or more people in a cohort comprising the new applicant and the one or more associates;

assigning, by the computer, a weight to each of the identified indicators of fraud using a classification model; and

calculating, by the computer, a risk score for the new applicant using each of the weights assigned to the one or more identified fraud indicators.

2. The method according to claim 1, further comprising determining, by the computer, whether the new applicant is a same person as a prior applicant in the first database,

wherein the new applicant is the same person as the prior applicant when a subset of the one or more characteristics of the new applicant substantially matches the subset of the one or more characteristics associated with the prior applicant.

3. The method according to claim 1, further comprising identifying, by the computer, an indicator of fraud associated with a person in the cohort from a second data source according to a model for a type of fraud.

4. The method according to claim 1, further comprising identifying, by the computer, from a search of a second data source an associate of the new applicant having a relationship with the new applicant based on one or more characteristics in common with the new applicant,

wherein the cohort of people further comprises the associate of the new applicant.

5. The method according to claim 4, further comprising:

assigning, by the computer, a second weight to each of the one or more common characteristics defining a relationship between an associate and the new applicant;

determining, by the computer, a strength of the relationship between the new applicant and the associate using the second weight of each of the one or more common characteristics, wherein the cohort comprises the associated only when the strength of the relationship with the new applicant satisfies a relationship threshold.

6. The method according to claim 5, wherein the risk score for the cohort is further determined using the strength of one or more relationships in the cohort.

7. The method according to claim 1, wherein a characteristic of an applicant is selected from the group consisting of:

a name, a derivative of a name, a home address, an work address, a prior address, a familiar relation, a social security number, derivative of a social security number, and a criminal history.

8. The method according to claim 1, wherein an indicator of fraud of the cohort is selected from the group consisting of:

a criminal history of a person in the cohort, a

9. The method according to claim 1, further comprising receiving, by the computer, a data source comprising one or more characteristics associated with a person in the cohort from a data-mining program automatically searching one or more data sources of a public network.

10. The method according to claim 1, further comprising receiving, by the computer, a data source comprising one or more indicators of fraud associated with the cohort from a data mining program automatically searching one or more data sources of a public network.

11. A benefits provider application system configured to mitigate fraud by a cohort, the system comprising:

a provider application database storing in memory one or more applications received from one or more prior applicants seeking to distribute a government benefit, wherein each prior applicant is associated with one or more attributes; and

a server comprising a processor configured to;

receive a new application from a new applicant having one or more attributes;

identify one or more associates having a relationship with the new applicant from the one or more prior applicants, wherein the relationship between an associate and the new applicant is based upon one or more common attributes;

identify one or more indicators of fraud for the new applicant and each of the one or more associates using one or more fraud models identifying a set of one or more attributes as being indicators of fraud; and

determine a risk factor for the new applicant based upon a risk score determined by the one or more indicators of fraud identified for the new applicant and each of the one or more associates.

12. The system according to claim 11, wherein the one or more attributes are selected from the group consisting of:

characteristics of a person, a work history, a criminal history, a personal history, and a residence history.

13. The system according to claim 11, further comprising one or more government databases storing data comprising one or more attributes of the one or more prior applicants.

14. The system according to claim 11, further comprising one or more open sources having data comprising one or more attributes of the new applicant.

15. The system according to claim 14, wherein a web crawler program searches the one or more open sources for data comprising attributes of the new applicant and an associate of the new applicant having a relationship with the new applicant based on one or more common attributes.

16. The system according to claim 11, wherein the server determines a strength of a relationship based upon weights assigned to each of the attributes common between the associated and the new applicant according to a relationship model.

17. The system according to claim 11, wherein the server assigns a weight to each of the one or more identified indicators of fraud according to the one or more fraud models.

18. The system according to claim 17, wherein the server determines the risk score for a cohort of people comprising the associates and the new applicant using each of the weights assigned to the one or more indicators of fraud.