Combat fraudulent insurance claims and billing anomalies with advanced machine learning. Our AI-powered platform identifies suspicious patterns in real-time, protecting your organization from costly fraud and abuse.
Comprehensive tools to identify, investigate, and prevent healthcare fraud at scale
Machine learning models analyze billing patterns across millions of claims to identify unusual behavior. Recognizes billing cycles, provider specialty patterns, patient demographics, and claim sequences to flag anomalies automatically.
Automatic identification of statistical outliers in claims data. Detects unusual service frequencies, billing amounts, diagnosis combinations, and procedure clustering that deviate from established norms.
Instant notifications for suspected fraudulent activities with configurable alert thresholds. Provides alerts via email, SMS, and dashboard for immediate investigation and intervention before claims are processed.
Deep-dive analysis of claim attributes: medical necessity validation, unbundling detection, upcoding identification, and duplicate claim discovery. Correlates claims across time, providers, and patient records.
Automated risk profiles for each provider based on historical behavior, claim patterns, and fraud indicators. Helps identify high-risk providers for enhanced monitoring and targeted audits.
Identifies connected fraud rings and related parties through network mapping. Tracks relationships between providers, clinics, patient groups, and billing entities to uncover organized fraud schemes.
Comprehensive investigator workspace with document review, case management, timeline visualization, and evidence collection. Track investigation progress and maintain audit trails for regulatory compliance.
Automated generation of fraud reports for regulatory agencies, compliance with OIG/CMS requirements, and documentation for legal proceedings. Supports multiple reporting formats and regulatory standards.
Significant financial and operational benefits from comprehensive fraud detection
Catch fraudulent claims before they're paid. Our AI algorithms identify 95% of attempted fraud with industry-leading accuracy, preventing financial losses and protecting your claims budget.
Prevent fraudulent claims from reaching your claims budget. Organizations typically recover 2-3% of annual claims volume through fraud prevention, equating to millions in savings for large payers.
Automated analysis and prioritization reduces manual investigation workload. Investigators focus on high-probability cases with AI-generated evidence packages, accelerating case closure and enforcement action.
Machine learning models predict future fraud attempts before they happen. Early identification of emerging patterns and provider behavior changes enables proactive intervention and prevention.
Advanced algorithms trained on legitimate billing patterns minimize false positive rates to under 5%. Reduces investigation burden and prevents wrongful provider accusations through intelligent filtering.
Real-time fraud detection operates around the clock without human intervention. Alerts your team immediately to suspicious activities, enabling same-day response to potential fraud schemes.
Get answers about our fraud detection capabilities and implementation
Our fraud detection system maintains a false positive rate below 5%, meaning high confidence in flagged cases. This is achieved through continuous model refinement using your legitimate billing patterns as baseline. Cases are categorized by risk level (low, medium, high), allowing your team to prioritize high-confidence fraud cases for investigation. We provide detailed scoring logic for each alert so investigators understand why a claim was flagged and can make informed decisions about investigation priority.
Our fraud analytics platform integrates data from multiple sources: claims data (headers and line items), provider information (credentials, specialties, locations), patient records (demographics, history, diagnosis patterns), payment data (posting patterns, timelines), eligibility information, and external regulatory databases (OIG exclusions, sanctions). The system correlates data across these sources to identify complex fraud patterns. We support integration with your existing claims management system, EHR, and data warehouse through standard interfaces like HL7 and database APIs.
Our models undergo continuous retraining using recent claims data and confirmed fraud cases from your organization and industry-wide data. We employ ensemble learning combining multiple algorithms: unsupervised anomaly detection (identifies novel patterns), supervised classification (learns from confirmed fraud cases), and graph analysis (detects network relationships). Models adapt to seasonal variations, emerging fraud methods, and your organization's evolving legitimate billing patterns. Quarterly model updates ensure the system remains effective against evolving fraud techniques.
The platform is designed to support compliance with CMS, OIG, and state insurance fraud investigation requirements. Features include: comprehensive audit trails documenting all analysis and decisions, investigation case management with evidence documentation, regulatory reporting for OIG and state authorities, False Claims Act compliance tracking, and legal hold functionality for litigation support. We maintain industry certifications and undergo regular compliance audits. The system provides training and guidelines for proper fraud investigation procedures to ensure your team follows legal requirements throughout the investigation process.
See how AI-powered analytics can identify and prevent healthcare fraud at scale