Machine learning models detecting anomalous claims patterns, identifying suspicious provider behavior, and flagging potential fraud before payment execution.
Anomaly detection algorithms identifying deviations from normal billing patterns, service frequency, and diagnostic-procedural relationships
Provider and member fraud risk scores incorporating historical behavior, peer comparisons, and known scheme indicators
Pre-built detection rules for common fraud types — unbundling, upcoding, phantom billing, kickback arrangements
Ranked alerts with estimated financial exposure guiding Special Investigations Unit focus and resource allocation
Real-time fraud scoring blocking suspicious claims before payment with investigative workflow triggers
Continuous monitoring of billing patterns flagging outlier providers for enhanced scrutiny and audits
Estimation of recoverable amounts guiding legal action decisions and settlement negotiations
Identification of coordinated fraud networks involving multiple providers, members, and facilities