Detect Healthcare Fraud with AI Precision
Leverage advanced AI models to automatically identify billing fraud, upcoding, and abuse in medical claims—saving billions in healthcare costs and protecting program integrity.
Analysis Complete
1,000 radiology reports processed
Perfect Detection Rate
All 10 fraud patterns identified across 20 test cases
The Healthcare Fraud Crisis
Fraud, Waste, and Abuse (FWA) costs the healthcare industry billions annually, diverting critical resources from patient care.
Estimated annual cost of healthcare fraud in the United States alone
Healthcare fraud represents up to 10% of total healthcare expenditures
Traditional methods can only review a fraction of submitted claims
Key Capabilities
Advanced AI-powered analysis to catch what manual review misses
Upcoding Detection
Identifies billing code mismatches like billing for contrast CT when non-contrast was performed
Temporal Anomalies
Catches future-dated services, impossible timestamps, and timing inconsistencies
Compliance Gaps
Identifies missing signatures, phantom patients, and documentation deficiencies
Clinical Inconsistencies
Detects anatomical impossibilities and finding/diagnosis mismatches
Provider Fraud
Flags phantom providers, suspicious credentials, and provider-specific patterns
Multi-Model Analysis
Cross-validates findings using multiple AI models for maximum accuracy
See the AI in Action
Explore our comprehensive dashboard showing detection results across 1,000 radiology reports, or try the live demo with your own documents.