Enterprise Insurance Fraud Detection System
Real-time **6-agent ensemble analysis** combining pattern recognition, document forensics, behavioral analysis, and network graph traversal. Achieves **95.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Pattern Recognition Agent
Identifying whether a new claim matches historical fraud patterns requires searching through thousands of past cases efficiently.
Core Logic
Implements **Cosine Similarity with TF-IDF weighting** algorithm (O(m×n) complexity with early termination optimization). Searches 50,000+ historical fraud cases. Returns top 10 similar cases with confidence scores. Identifies fraud typology matches: forged wills (47%), identity theft (24%), fraud rings (18%).
Document Authenticity Agent
Sophisticated document forgeries can fool manual review. Detecting forged death certificates, wills, and identity documents requires multi-factor forensic analysis.
Core Logic
Applies **5-factor forensic scoring** in O(1) constant time: metadata consistency (30%), digital signature validation (25%), **Error Level Analysis (ELA)** for image manipulation (20%), text pattern analysis (15%), and registry verification (10%). Outputs 0-100 authenticity score with detailed breakdown.
Behavioral Analysis Agent
Fraudsters exhibit abnormal behavior patterns like rapid beneficiary changes, unusual login patterns, and claim timing anomalies that are invisible to rule-based systems.
Core Logic
Deploys **Z-Score statistical anomaly detection** (O(n) time-series analysis) on behavioral signals: beneficiary change velocity, policy modification frequency, login patterns, claim timing relative to policy events. Flags anomalies with severity ratings: CRITICAL, HIGH, MEDIUM, LOW.
Network Analysis Agent
Organized fraud rings coordinate attacks across multiple policies using shared infrastructure (bank accounts, addresses, devices) that are invisible in isolated claim analysis.
Core Logic
Implements **BFS graph traversal** (O(V+E) complexity) for fraud ring detection. Analyzes connection types with weighted confidence: same bank account (95%), shared address (85%), common device (80%), IP correlation (70%), phone linkage (60%). Visualizes network structure and identifies ring leaders.
Risk Scoring Agent
Combining multiple fraud signals into a unified risk score requires calibrated weighting and confidence interval estimation.
Core Logic
Applies **Weighted Logistic Regression** model (v2.4.1, validated on 50,000+ cases) with O(n) complexity. Feature weights: Document Authenticity (-2.8, 35%), Pattern Similarity (2.4, 30%), Behavioral Anomaly (1.9, 20%), Network Risk (1.5, 15%). Outputs 0-100 risk score with **95% confidence intervals**.
Performance Monitor Service
Real-time fraud detection requires sub-second latency and high availability. System degradation must be detected and mitigated automatically.
Core Logic
Implements **Circuit Breaker pattern** with exponential backoff. Maintains **LRU caching** (92-96% hit rate) for frequent queries. Tracks P50/P95/P99 latency metrics. Sustains **1,247 claims/second throughput**. Health scoring: HEALTHY (≥95), DEGRADED (80-94), CRITICAL (<80).
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
4 technologies
Architecture Diagram
System flow visualization