AI-Powered Claims Fraud Detection & Investigation
Deploys a **real-time six-agent investigation team** that analyzes documents, detects patterns, maps fraud networks, profiles behavioral anomalies, and synthesizes findings into explainable risk scores. Features autonomous preventive actions, predictive alerts for emerging fraud patterns, and continuous learning from investigator feedback.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Fraud Detection Dashboard - Agent orchestration interface with claim prioritization showing high, medium, and low-risk cases for investigation
Live Fraud Analysis - Real-time agent execution showing document analysis, pattern detection, and red flag identification with reasoning steps
Investigation Summary - Comprehensive fraud analysis results with risk features, ensemble model scoring, and explainability metrics
Analysis Complete - Final fraud verdict with confidence score, risk breakdown by category, key findings, and detailed agent performance
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Fraud investigations require **rapid coordination of specialized analysis** while maintaining audit trails, managing escalations, and ensuring consistent decision-making across thousands of daily claims.
Core Logic
Acts as master coordinator deploying and monitoring all investigation agents. Manages session state, coordinates inter-agent messaging, makes escalation decisions based on risk thresholds, triggers autonomous actions, and ensures all findings are consolidated into the final fraud assessment with complete audit trail.
Document Analyzer Agent
Fraudulent claims often involve **manipulated or fabricated documents**—altered invoices, photoshopped damage photos, forged medical records—that humans cannot reliably detect at scale.
Core Logic
Performs LLM-based document analysis with specialized manipulation detection. Extracts text and metadata, analyzes image authenticity, detects digital alterations, identifies inconsistencies between documents, and scores document credibility. Flags AI-generated content and suspicious metadata patterns.
Pattern Detector Agent
Sophisticated fraud follows **recognizable patterns** learned from historical cases—claim timing, damage types, provider combinations—but rules-based systems cannot adapt to evolving fraud tactics.
Core Logic
Applies ML pattern matching to identify fraud signatures in claim data. Compares against historical fraud patterns, detects anomalous combinations of claim attributes, calculates pattern match confidence, and identifies novel patterns for human review. Continuously learns from confirmed fraud cases.
Network Analyzer Agent
**Organized fraud rings** involve multiple claimants, witnesses, providers, and even internal employees coordinating across seemingly unrelated claims. Traditional analysis examining claims individually cannot detect these networks.
Core Logic
Performs graph-based fraud ring detection by analyzing entity relationships across claims. Maps connections (same address, phone, bank account, witness appearances), calculates network centrality scores, identifies suspicious clustering patterns, and visualizes fraud networks. Estimates ring expansion potential for proactive intervention.
Behavioral Profiler Agent
Fraudulent claimants exhibit **behavioral anomalies**—inconsistent statements, timeline discrepancies, stress indicators—that trained investigators recognize but cannot evaluate at scale.
Core Logic
Detects behavioral anomalies through statement analysis, timeline consistency checking, and communication pattern evaluation. Identifies stress/confidence indicators in written and verbal statements, detects contradictions across claim touchpoints, and profiles claimant behavior against baseline patterns.
Risk Synthesizer Agent
Translating multiple agent findings into a **defensible, explainable fraud decision** requires sophisticated aggregation that weights evidence appropriately and produces recommendations investigators and regulators can understand.
Core Logic
Aggregates all agent findings into final fraud risk assessment. Calculates overall risk score with confidence intervals, produces SHAP-like feature contributions explaining each factor's impact, generates percentile ranking against claim population, and creates investigation roadmap with prioritized tasks. Outputs include regulatory compliance status and financial impact analysis.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization