Artificial intelligence techniques for identifying identity manipulation

Patent metadata

Field	Value
Publication number	US-12561446-B2
Application number	US-202318460415-A
Country	US
Kind code	B2
Filing date	Sep 1, 2023
Priority date	Sep 1, 2023
Publication date	Feb 24, 2026
Grant date	Feb 24, 2026

Abstract

A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.

First claim

What is claimed is:

1 . A system comprising:
a processor; and a non-transitory computer-readable medium comprising instructions that are executable by the processor to cause the processor to perform operations comprising:
receiving entity data and interaction data associated with a target entity;
determining, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models;
generating a first graph structure of a linked graph structure using the entity data, wherein the first graph structure represents identity data about the target entity;
generating a second graph structure of the linked graph structure using the interaction data, wherein the second graph structure represents historical interaction data associated with the target entity;
linking the first graph structure and the second graph structure to form the linked graph structure by integrating the entity data and the identity data, wherein one or more identities indicated by the first graph structure are associated with one or more interactions of the historical interaction data of the second graph structure;
applying the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity;
determining, based on the risk indicator, that the target entity is associated with malicious behavior; and
preventing, based on determining that the target entity is associated with malicious behavior, the target entity from accessing an interactive computing environment.

2 . The system of claim 1 , wherein the one or more risk signals indicate a likelihood that an identity of the target entity is manipulated.

3 . The system of claim 1 , wherein the one or more artificial intelligence models comprise a plurality of machine-learning models that comprises at least one clustering machine-learning model and at least one graph mining machine-learning model.

4 . The system of claim 1 , wherein the operation of applying the one or more risk signals to the linked graph structure comprises clustering data underlying the linked graph structure to determine the risk indicator, and wherein the data underlying the linked graph structure comprises the one or more risk signals.

5 . The system of claim 1 , wherein the operations further comprise providing a responsive message based on the risk indicator by controlling an interaction involving the target entity using the risk indicator.

6 . The system of claim 1 , wherein the entity data comprises identity information about the target entity, wherein the identity information comprises name information, account information, and device information associated with the target entity, and wherein the interaction data comprises information about previously executed interactions involving the target entity.

7 . A method comprising:
receiving, by a computing device, entity data and interaction data associated with a target entity; determining, by the computing device and based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models; generating, by the computing device, a first graph structure of a linked graph structure using the entity data, wherein the first graph structure represents identity data about the target entity; generating, by the computing device, a second graph structure of the linked graph structure using the interaction data, wherein the second graph structure represents historical interaction data associated with the target entity; linking, by the computing device, the first graph structure and the second graph structure to form the linked graph structure by integrating the entity data and the identity data, wherein one or more identities indicated by the first graph structure are associated with one or more interactions of the historical interaction data of the second graph structure; applying, by the computing device, the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity; determining, based on the risk indicator, that the target entity is associated with malicious behavior; and preventing, based on determining that the target entity is associated with malicious behavior, the target entity from accessing an interactive computing environment.

8 . The method of claim 7 , wherein the one or more risk signals indicate a likelihood that an identity of the target entity is manipulated.

9 . The method of claim 7 , wherein the one or more artificial intelligence models comprise a plurality of machine-learning models that comprises at least one clustering machine-learning model and at least one graph mining machine-learning model.

10 . The method of claim 7 , wherein applying the one or more risk signals to the linked graph structure comprises clustering data underlying the linked graph structure to determine the risk indicator, and wherein the data underlying the linked graph structure comprises the one or more risk signals.

11 . The method of claim 7 , further comprising providing a responsive message based on the risk indicator by controlling an interaction involving the target entity using the risk indicator.

12 . The method of claim 7 , wherein the entity data comprises identity information about the target entity, wherein the identity information comprises name information, account information, and device information associated with the target entity, and wherein the interaction data comprises information about previously executed interactions involving the target entity.

13 . A non-transitory computer-readable medium comprising instructions that are executable by a processing device for causing the processing device to perform operations comprising:
receiving entity data and interaction data associated with a target entity; determining, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models; generating a first graph structure of a linked graph structure using the entity data, wherein the first graph structure represents identity data about the target entity; generating a second graph structure of the linked graph structure using the interaction data, wherein the second graph structure represents historical interaction data associated with the target entity; linking the first graph structure and the second graph structure to form the linked graph structure by integrating the entity data and the identity data, wherein one or more identities indicated by the first graph structure are associated with one or more interactions of the historical interaction data of the second graph structure; applying the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity; determining, based on the risk indicator, that the target entity is associated with malicious behavior; and preventing, based on determining that the target entity is associated with malicious behavior, the target entity from accessing an interactive computing environment.

14 . The non-transitory computer-readable medium of claim 13 , wherein the one or more risk signals indicate a likelihood that an identity of the target entity is manipulated, and wherein the one or more artificial intelligence models comprise a plurality of machine-learning models that comprises at least one clustering machine-learning model and at least one graph mining machine-learning model.

Assignees

• Equifax Inc

Inventors

• Shen Cuizhen
• Ranganathan Arunkumar
• Lim Daniel
• Munguia Philip

Classifications

• G06F2221/034

  Test or assess a computer or a system · CPC title

• G06Q50/265

  Personal security, identity or safety · CPC title

• G06Q30/0185

  Product, service or business identity fraud · CPC title

• G06Q10/0635

  Risk analysis of enterprise or organisation activities · CPC title

• G06F16/9024

  Graphs; Linked lists (G06F16/9027 takes precedence) · CPC title