Dynamic Graph Network Flow Analysis for Malfeasance Detection
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Solution Overview
Problem
Complex networks of entities and their connections make it difficult to identify anomalous trends and malfeasance patterns in real time, as existing methods lack the efficiency and accuracy needed for timely detection and remediation.
Innovation Solution
A system employing dynamic directed graph analysis to generate and monitor graphs representing networked accounts and transactions, using reputation values, confidence values, and anomaly detection to identify and remediate potential malfeasance by collapsing interconnected nodes into super nodes and analyzing network flow patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning systems are implemented to analyze complex networks, then identification accuracy of malfeasance patterns improves, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex network analysis into multiple hierarchical levels (node level, edge level, subgraph level, and full graph level). Each level processes specific features independently, allowing the system to maintain high identification accuracy while managing computational complexity through divide-and-conquer strategy.
Solution Approach 2:
The system transforms the network analysis problem into multiple dimensional layers by creating hierarchical representations from individual nodes up to the complete graph. This multi-dimensional approach enables parallel processing at different levels, improving accuracy without linearly increasing overall system complexity.
2Productivity
If real-time monitoring of dynamic graphs is performed, then detection speed of malfeasance improves, but computational load and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing network data into hierarchical structures before actual malfeasance detection occurs. Historical patterns are pre-computed and stored, enabling rapid real-time detection without excessive processing delays during critical monitoring periods.
Solution Approach 2:
The system implements dynamic graph analysis that adapts processing intensity based on real-time conditions. The hierarchical framework allows the system to dynamically adjust which levels require full analysis versus which can use cached results, optimizing the balance between detection speed and processing time.
Data Source
AI summary
A system is configured for extracting historical information for a first plurality of resource pools, generating a historical dynamic graph based on the historical information, identifying from the historical information, a historical set of resource distribution events associated with a malfeasance, determining from the historical dynamic graph, at least one historical malfeasance pattern, receiving current resource distribution request information for a second plurality of resource pools, generating a current dynamic graph comprising a current plurality of nodes and a current plurality of edges, monitoring the current dynamic graph and identify a current malfeasance pattern, and executing one or more remediation actions on one or more of the second plurality of resource pools associated with the current dynamic graph.