A Data-Driven Intelligent Supervision System for Generating High-Risk Organized Fraud Clues in Medical Insurance Funds

Medical insurance fraud, especially organized drug resale schemes, has become increasingly sophisticated, challenging traditional supervision methods. This paper presents an AI-powered legal supervision model that automatically detects fraudulent drug resale activities in medical insurance claims. Unlike rule-based approaches, our solution employs multi-dimensional behavioral analysis and adaptive clustering techniques to identify both individual anomalies and organized fraud networks. The proposed model follows a three-stage detection pipeline: (1) automated clue generation through feature aggregation across frequency, cost, and behavioral dimensions; (2) group behavior analysis using spatiotemporal patterns and medication similarity metrics; (3) risk stratification via FLASC clustering to dynamically determine suspicion thresholds. Key innovations include a data-driven threshold generation mechanism that eliminates expert bias and a cross-dimensional fraud pattern recognition system that connects individual outliers with group behaviors. Validated on real-world medical insurance data (8917 insurance cards, 1.1 million records), the model achieved 89% precision, 42% recall, and 87% accuracy in detecting high-risk fraud cases while uncovering previously unnoticed organized fraud rings. This research provides a scalable framework for intelligent healthcare fund supervision, with potential applications in other social security domains.