Illustrations / US Casualty Reinsurer
Illustrated workflow: Claims Intelligence
This is a teaching scenario for a fictional desk, not a customer testimonial. Figures in the source story are not audited results and must not be quoted as ROI.
Situation
A US-based casualty reinsurer was processing 15,000+ claims notifications annually with suspected fraud rates of 8-12%. Manual investigation was slow, inconsistent, and missed sophisticated fraud schemes. The company was paying $15M+ annually in fraudulent claims.
Workflow
Implemented Claims Intelligence Agent to analyze loss notifications, identify suspicious patterns, and flag high-risk claims for investigation. The system uses anomaly detection, network analysis, and historical pattern matching across 20 years of claims data.
- Claims Agent
- Fraud Detection Agent
- Legal Agent
Steps
- Phase 1: Historical Training. Trained Claims Agent on 20 years of claims data, including 500+ confirmed fraud cases. Built pattern recognition models for medical fraud, staged accidents, and inflated claims.
- Phase 2: Real-Time Scoring. Deployed real-time fraud scoring system analyzing incoming loss notifications. Integrated with claims management platform and SIU workflows.
- Phase 3: Network Analysis. Added network graph analysis to identify fraud rings and connected claims patterns across multiple cedents and jurisdictions.
Illustrated scenario, not a named customer: “The fraud patterns we're catching now would have been impossible to detect manually. This is a game-changer for our bottom line.”