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Fraud Detection
Real-time insurance fraud prevention using ensemble ML models. 98% accuracy on known patterns.
How It Works
Ensemble model combining Random Forest and XGBoost with 98% accuracy on known fraud patterns. Analyzes billing codes, diagnosis-treatment matching, and claim frequency anomalies. Real-time insurance database cross-reference and behavioral pattern recognition. Prevents Sh10M+ in fraudulent claims annually.
Key Benefits
- Stops anomalous claims before billing.
- Cross-references historical patient treatments.
- Protects hospital revenue streams.