Context Node Sister Identification for Fraud Detection
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Solution Overview
Problem
Detecting fraudulent or criminal activity in financial transactions is challenging due to the large amount of information involved and the ease of transaction, making it difficult to identify improper activity effectively.
Innovation Solution
A computer-implemented method uses a context node to identify sister nodes in a network graph by generating a network graph from input data, selecting a context node, determining patterns, and outputting sister nodes with similar characteristics, allowing for the detection of fraudulent activity patterns across different scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect fraudulent activity, then the system is simple to operate, but it cannot effectively identify improper activity due to the large amount of information involved
Solution Approach 1:
The patent segments the complex fraud detection problem into multiple components: network graph construction from transaction data, pattern definition and detection modules, and sister node identification. This segmentation allows the system to handle large amounts of information systematically while maintaining detection accuracy through structured analysis of transaction relationships.
Solution Approach 2:
The patent introduces an intermediary processing layer that transforms raw transaction data into network graphs, which then serve as intermediaries for pattern detection. This intermediary representation simplifies the complex relationships in transaction data, enabling effective fraud detection without requiring direct analysis of all raw data.
2Measurement precision
If comprehensive transaction data is analyzed to detect fraud, then detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-defining fraud patterns and pre-constructing network graphs from transaction data. These preliminary structures enable rapid pattern matching and sister node identification during actual fraud detection, reducing processing time while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent creates simplified copies of transaction relationships through network graphs, where complex transaction data is represented as nodes and edges. This copying approach allows efficient pattern matching and fraud detection by working with the graph representation rather than the full complexity of original transaction data.
3Productivity
If manual analysis of transaction patterns is performed, then false positives are reduced, but productivity and coverage of fraud detection decrease
Solution Approach 1:
The patent implements feedback mechanisms where detected patterns and sister nodes provide information back to refine the detection process. The system learns from identified fraud patterns and uses this feedback to improve future detections, maintaining high reliability while achieving automated high-throughput processing.
Solution Approach 2:
The system performs self-service by automatically identifying fraud patterns and sister nodes without requiring manual analysis for each case. The automated pattern detection and sister node identification capabilities enable high productivity while maintaining detection reliability through consistent application of defined patterns.
Data Source
AI summary
A computer-implemented method uses a context node to identify sister nodes. The method includes receiving, by a processor, input data. The input data includes a plurality of messages, each message containing a set of message data. The method further includes, generating, by a pattern detector, and based on the input data, a network graph, where the network graph includes a plurality of nodes. The method also includes selecting a first context node. The method includes determining a first pattern for the first context node. The method further includes identifying, based on the first pattern, a first sister node. The method also includes outputting, by a network interface, the first sister node and the network graph.