Systems and methods for identifying entities using geographical and social mapping

• US 10,438,308 B2
• Filed: 07/05/2016
• Issued: 10/08/2019
• Est. Priority Date: 02/04/2003
• Status: Active Grant

• Current Assignee: LexisNexis Risk Solutions, Inc. (RELX PLC)
• Original Assignee: LexisNexis Risk Solutions, Inc. (RELX PLC)
• Inventors: Prichard, Johannes Philippus de Villiers, Shaw, Jesse C P B, Bayliss, David Alan
• Primary Examiner(s): Cervetti, David Garcia
• Application Number:
  US15/202,099
• Publication Number:
  US 20160328814A1
• Time in Patent Office: 1,190 Days
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 50/265 Personal security, identity or safety
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal

Abstract

Embodiments of the disclosed technology include systems and methods for identifying one or more entities associated with activities. In an example implementation, a method includes determining one or more geographical regions proximate to the plurality of locations associated with the one or more activities; determining connections between one or more identities of a population and a plurality of related entities associated with the one or more identities; determining geographical information associated with related entities; weighting one or more metrics for each of the identities based on the geographical information associated with the related entities and the or more geographical regions proximate to the plurality of locations associated with the one or more activities; scoring the one or more weighted metrics; and providing, based on the scoring, an indication of a likelihood that the one or more identities of the population are associated with the one or more activities.

Claims

1. A computer-implemented method for identifying one or more entities associated with one or more fraudulent activities, the method comprising:

determining, by at least one processor configured to access at least one online network, and from a plurality of locations associated with one or more online activities, one or more geographical regions proximate to the plurality of locations;

determining, from a system graph and by the at least one processor, connections between one or more identities of a population and a plurality of related entities associated with the one or more identities, wherein the connections are determined based on common relations in the system graph;

determining geographical information associated with the plurality of related entities;

determining one or more physical addresses associated with one or more incidents of identity theft;

weighting, by the at least one processor, one or more metrics for at least a portion of the one or more identities of the population based at least in part on the geographical information associated with the plurality of related entities and the one or more geographical regions proximate to the plurality of locations associated with the one or more activities, wherein the weighting the one or more metrics comprises applying a measure of common connections between the one or more identities and the plurality of related entities based on a distance between the geographical regions proximate to the plurality of locations associated with the one or more activities and the one or more physical addresses associated with one or more incidents of identity theft; and

outputting, based on the weighting, an indication of a likelihood that the one or more identities of the population are associated with one or more fraudulent activities related to identity theft.

2. The method of claim 1, wherein determining connections between the one or more identities of a population and a plurality of related entities associated with the one or more identities comprise determining one or more of relative connections, business associate connections, social network connections, and historical address connections.

3. The method of claim 1, wherein the system graph comprises information associated with one or more public records including one or more of housing records, vehicular records, marriage records, divorce records, hospital records, death records, court records, property records, employment records, business records, address records, tax records, corporate records, incarceration records, and utility records, wherein the utility records comprise one or more of utility hookups, disconnects, and associated service addresses.

4. The method of claim 1, wherein weighting the one more metrics comprises applying a weight corresponding to the geographical information associated with the plurality of related entities.

5. The method of claim 1, wherein weighting the one more metrics comprises applying a weight corresponding to a distance between the one or more identities of the population and the plurality of related entities associated with the one or more identities.

6. The method of claim 1, wherein determining a geographical region proximate to the plurality of locations associated with the one or more activities comprises determining a series of concentric areas overlapping an address.

7. The method of claim 6, wherein the concentric areas overlapping the address are determined at least in part based on features comprising one or more of local natural formations, man-made formations, streets, walking areas, population, businesses, and neighborhoods.

8. A system comprising:

at least one memory for storing data and computer-executable instructions; and

at least one processor configured to access the at least one memory and at least one online network, and further configured to execute the computer-executable instructions to;

determine, from a plurality of locations associated with one or more online activities, one or more geographical regions proximate to the plurality of locations;

determine, from a system graph, and by the at least one processor, connections between one or more identities of a population and a plurality of related entities associated with the one or more identities, wherein the connections are determined based on common relations in the system graph;

determine geographical information associated with the plurality of related entities;

determine one or more physical addresses associated with one or more incidents of identity theft;

weight, with the at least one processor, one or more metrics for at least a portion of the one or more identities of the population based at least in part on the geographical information associated with the plurality of related entities and the one or more geographical regions proximate to the plurality of locations associated with the one or more activities, wherein the weighting the one or more metrics comprises applying a measure of common connections between the one or more identities and the plurality of related entities based on a distance between the geographical regions proximate to the plurality of locations associated with the one or more activities and the one or more physical addresses associated with one or more incidents of identity theft; and

output, based on the weighting, an indication of a likelihood that the one or more identities of the population are associated with one or more fraudulent activities related to identity theft.

9. The system of claim 8, wherein determining connections between the one or more identities of a population and a plurality of related entities associated with the one or more identities comprise determining one or more of relative connections, business associate connections, social network connections, and historical address connections.

10. The system of claim 8, wherein the system graph comprises information associated with one or more public records including one or more of housing records, vehicular records, marriage records, divorce records, hospital records, death records, court records, property records, employment records, business records, address records, tax records, corporate records, incarceration records, and utility records, wherein the utility records comprise one or more of utility hookups, disconnects, and associated service addresses.

11. The system of claim 8, wherein weighting the one more metrics comprises applying a weight corresponding to a distance between the geographical regions proximate to the plurality of locations associated with the one or more activities and the geographical information associated with the plurality of related entities.

12. The system of claim 8, wherein weighting the one more metrics comprises applying a weight corresponding to a distance between the one or more identities of the population and the plurality of related entities associated with the one or more identities.

13. The system of claim 8, wherein determining a geographical region proximate to the plurality of locations associated with the one or more activities comprises determining a series of concentric areas overlapping an address.

14. The system of claim 13, wherein the concentric areas overlapping the address are determined at least in part based on features comprising one or more of local natural formations, man-made formations, streets, walking areas, population, businesses, and neighborhoods.

15. A non-transitory computer-readable medium that stores instructions which, when executed by at least one processor in a system, cause the system to perform a method comprising:

determining, by at least one processor configured to access at least one online network, and from a plurality of locations associated with one or more online activities, one or more geographical regions proximate to the plurality of locations;

determining, from a system graph and by the at least one processor, connections between one or more identities of a population and a plurality of related entities associated with the one or more identities, wherein the connections are determined based on common relations in the system graph;

determining geographical information associated with the plurality of related entities;

determining one or more physical addresses associated with one or more incidents of identity theft;

weighting, by the at least one processor, one or more metrics for at least a portion of the one or more identities of the population based at least in part on the geographical information associated with the plurality of related entities and the one or more geographical regions proximate to the plurality of locations associated with the one or more activities, wherein the weighting the one or more metrics comprises applying a measure of common connections between the one or more identities and the plurality of related entities based on a distance between the geographical regions proximate to the plurality of locations associated with the one or more activities and the one or more physical addresses associated with one or more incidents of identity theft; and

outputting, based on the weighting, an indication of a likelihood that the one or more identities of the population are associated with one or more fraudulent activities related to identity theft.

16. The non-transitory computer-readable medium of claim 15, wherein determining connections between the one or more identities of a population and a plurality of related entities associated with the one or more identities comprise determining one or more of relative connections, business associate connections, social network connections, and historical address connections.

17. The non-transitory computer-readable medium of claim 15, wherein the system graph comprises information associated with one or more public records including one or more of housing records, vehicular records, marriage records, divorce records, hospital records, death records, court records, property records, employment records, business records, address records, tax records, corporate records, incarceration records, and utility records, wherein the utility records comprise one or more of utility hookups, disconnects, and associated service addresses.

18. The non-transitory computer-readable medium of claim 15, wherein weighting the one more metrics comprises applying a weight corresponding to a distance between the geographical regions proximate to the plurality of locations associated with the one or more activities and the geographical information associated with the plurality of related entities, and wherein weighting the one more metrics comprises applying a weight corresponding to a distance between the one or more identities of the population and the plurality of related entities associated with the one or more identities.

19. The non-transitory computer-readable medium of claim 15, wherein determining a geographical region proximate to the plurality of locations associated with the one or more activities comprises determining a series of concentric areas overlapping an address.

20. The non-transitory computer-readable medium of claim 15, wherein the concentric areas overlapping the address are determined at least in part based on features comprising one or more of local natural formations, man-made formations, streets, walking areas, population, businesses, and neighborhoods.