Medicare Fraud Detection for Qui Tam Attorneys — AI-Powered Billing Anomaly Analysis
Zervio.ai identifies Medicare billing anomalies at scale using statistical analysis, peer-group comparison, and machine learning — all from publicly available CMS data. We find the cases. You file them.
How It Works
Three-stage analytical pipeline processing millions of Medicare billing records
Multi-Source Data Fusion
We integrate five public CMS datasets — Part B billing, Part D prescribing, Open Payments, the OIG exclusion list, and the NPPES provider registry — covering 2021 through 2023.
Statistical Anomaly Detection
Z-score normalization against specialty peer groups, rule-based detection of impossible billing patterns, and gradient-boosted machine learning models identify providers whose patterns deviate significantly from peers.
Actionable Case Intelligence
Each provider receives a composite risk score (0–100), tiered classification, estimated overpayment analysis, and a detailed case narrative suitable for qui tam referral.
Why Partner With Zervio.ai
Purpose-built for False Claims Act qui tam referrals
Original Analysis
Independent statistical discovery from publicly available data. Not derived from insider tips or prior public disclosures.
Quantified Case Packages
Every case includes estimated overpayments, treble damage projections, and per-claim penalty calculations.
Publication Timestamps
All findings published with dates for first-to-file documentation. Indexed and cacheable for independent verification.
Continuous Monitoring
The platform runs continuously. New anomalies are detected as CMS publishes updated data each year.
Featured Analyses
Highest-scoring providers identified by our platform
Ready to review our findings?
Contact us to discuss partnership opportunities and access detailed case packages with full financial analysis.
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