Pairwise Account Graph Querying for Real-Time Fraud Linking
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Solution Overview
Problem
Conventional fraud detection systems in electronic transaction processing take significant time to identify fraud trends, leading to increased losses and damage to service provider brands, and there is a need for faster and more efficient identification of account takeover fraud and other fraud trends.
Innovation Solution
A pairwise graph traversal and merging system is employed to link and analyze account data using graph databases, enabling rapid identification of fraud trends through pairwise similarity computations, risk collusion detection, and compliance exploration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional fraud detection systems are used to identify and prevent account takeover fraud, then fraud detection coverage is maintained, but response time is significantly delayed and computing resources are excessively consumed
Solution Approach 1:
The patent segments the fraud detection process into distinct phases: real-time transaction monitoring, post-transaction analysis, and account linking operations. By dividing the large-scale graph analysis into smaller, manageable segments that can be processed independently and in parallel, the system achieves faster response times while maintaining comprehensive fraud detection coverage
Solution Approach 2:
The system performs preliminary actions by pre-computing account linkages and fraud patterns before actual fraud incidents occur. Account graphs are built and maintained in advance, allowing the system to quickly query pre-established relationships during transaction processing, thereby reducing detection time without sacrificing accuracy
2Measurement precision
If comprehensive account analysis is performed to identify fraud trends, then detection accuracy is improved, but computing resource consumption increases significantly
Solution Approach 1:
The patent extracts only the essential elements needed for fraud detection from the complete account data set. By identifying and extracting key account relationships, transaction patterns, and risk indicators, the system achieves high detection accuracy while processing a reduced data subset, thereby lowering computing resource consumption
Solution Approach 2:
The system applies partial analysis to the majority of accounts and full analysis only to high-risk accounts identified through initial screening. This selective approach maintains detection accuracy for critical cases while reducing overall computing resource consumption by avoiding exhaustive analysis of all accounts
3Reliability
If real-time fraud detection is implemented, then fraud prevention capability is enhanced, but system complexity increases
Solution Approach 1:
The patent implements a universal account graph framework that serves multiple functions: fraud detection, account linking, relationship analysis, and compliance monitoring. By designing a multi-functional system where the same infrastructure supports various fraud prevention needs, the system achieves high reliability without proportionally increasing complexity
Data Source
AI summary
There are provided systems and methods for pairwise graph querying, merging, and computing for account linking. A service provider may provide an account graph system to identify pairwise similarities between different accounts based on shared data that may be identified through one or more linking characteristics. When providing pairwise graph similarities, a service provider may receive a query identifying two or more accounts and/or an account with a parameter for graph exploration and querying. The service provider may utilize connection, link, or relationship graphs, queried and generated using a graph database, to determine pairwise similarities between the designated seed account and one or more selected accounts. The graph may include vertices for different queried data points and edges connecting such queries, where directionality of the edges or other vectors may be used to identify links or hops between accounts for data querying and exploration.