User Authentication Conflict Detection with Graph-Based Identity Analysis
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Solution Overview
Problem
Existing technologies face challenges in detecting and preventing unauthorized activities related to synthetic identities, which are increasingly sophisticated and difficult to identify.
Innovation Solution
A computing platform integrates public and private data sources using a graph database and machine learning to map user identity information, determine clusters, apply weights to relationship vectors, and generate a score indicating the validity of the claimed identity, transmitting alerts for potential conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional identity verification methods are used, then the system is simple to operate, but it cannot detect sophisticated synthetic identities
Solution Approach 1:
The patent combines multiple data sources (public data, private data, graph data) and analysis methods (clustering algorithms, machine learning models) into a unified identity verification system. This integration enables detection of synthetic identities by analyzing relationships and patterns across diverse data types, resolving the contradiction between detection accuracy and system complexity.
Solution Approach 2:
The patent replaces traditional rule-based identity verification with machine learning models and clustering algorithms. These automated systems analyze identity data patterns, detect anomalies, and identify synthetic identities without manual intervention, significantly improving detection precision while managing complexity through algorithmic automation.
2Measurement precision
If comprehensive data analysis is performed to detect synthetic identities, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary clustering and pattern recognition on identity data before final verification. By pre-processing data to identify potential synthetic identity patterns and relationships, the system reduces the computational burden during real-time verification, maintaining high detection accuracy while reducing processing time.
Solution Approach 2:
The machine learning models automatically analyze and classify identity data without requiring manual review for each case. The system self-adjusts to detected patterns and continuously improves detection capabilities, reducing processing time while maintaining or improving detection accuracy through automated adaptation.
Data Source
AI summary
Arrangements for an identity conflict detection model are provided. In some aspects, identity information associated with the user may be received from a computing device of a user. Graph data from a graph database that integrates data from a plurality of data sources, including public and private data, into a graph visualization may be retrieved. The received identity information may be mapped to the graph data by identifying, using a graph database engine, nodes and relationship vectors associated with the user. One or more clusters of the graph data may be determined. Weights indicating a degree of importance of a corresponding relationship vector in verifying a claimed identity of the user may be applied to the identified relationship vectors. A score may be generated indicating a likelihood that the claimed identity of the user is valid, and a corresponding notification may be sent.