Identify fraudulent billing patterns, wasteful care, and abusive practices with AI-powered anomaly detection that protects 1-2% of annual spend
FBI estimates healthcare fraud costs $80B annually—about 3% of total spend. Provider bills for services never rendered. Member uses dead relative's insurance card. Pharmacy dispenses brand drugs but bills for expensive specialty meds. Your claims system pays it all because nobody's watching.
Our Waste, Fraud, and Abuse Engine runs continuous behavioral analysis across providers, members, and pharmacies. Machine learning models flag statistical outliers, cross-reference claims against clinical plausibility, and prioritize investigations by fraud probability and financial impact.
Fraud Detection Algorithm// Provider behavioral analysis FOR each provider: pattern_score = ANALYZE( billing_frequency vs peers, diagnosis_code_distribution, upcoding_propensity, service_mix_anomalies, weekend_billing_spikes ) IF pattern_score > FRAUD_THRESHOLD: total_exposure = provider_payments_last_24mo FLAG "High-risk provider - investigate" // Member eligibility verification FOR each high_cost_claim: IF member_age inconsistent_with_diagnosis OR utilization_spike_after_coverage_start OR duplicate_member_id_usage_different_locations: FLAG "Identity fraud - verify eligibility" // Pharmacy abuse detection FOR each pharmacy: dispense_patterns = CHECK( brand_vs_generic_ratio, specialty_drug_concentration, early_refill_frequency, off_label_billing ) IF dispense_patterns indicate_systematic_overbilling: annual_overcharge = ESTIMATE_total_impact FLAG "Pharmacy investigation - recover" // Clinical plausibility check IF procedure NOT medically_necessary_for_diagnosis OR service_frequency exceeds_clinical_guidelines OR anatomically_impossible_claim: FLAG "Wasteful or fraudulent - deny/recover"
Upload your claims data. Our AI will flag suspicious patterns in under 24 hours. See exactly where fraud, waste, and abuse are costing you money.
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