Stop Fraud Before Payment Goes Out.Before Payment Goes Out.
Zabrizon's graph-neural-network fraud detection identifies billing fraud, provider collusion, and claims abuse patterns that rules-based systems miss — recovering an average of $4.20 per dollar invested.
Why Legacy Fraud Detection Fails Payers
Healthcare fraud is estimated at $300–950B annually in the US. Rules-based systems detect less than 1% of it before payment.
Rules-Based Systems Miss Adaptive Fraud
Fraudsters rapidly adapt to published detection rules. Static rule engines catch known patterns while new schemes pass through undetected.
High False-Positive Rates Waste Investigator Time
Legacy systems generate false-positive rates above 90% — investigators spend most of their time clearing legitimate claims rather than catching fraud.
Provider Collusion Is Invisible to Siloed Systems
Complex fraud networks involving multiple providers, laboratories, and DME suppliers require graph analytics to detect — not individual claim review.
Post-Payment Recovery Is Expensive and Low-Yield
Recovering overpayments after the fact costs $8–15 per dollar recovered. Pre-payment detection is 10–20× more cost-effective.
AI Fraud Detection Capabilities
Pre-payment, real-time, and retrospective fraud analytics that go far beyond rules.
Graph Neural Network Fraud Detection
Analyses relationships between providers, patients, billing agents, and pharmacies to surface collusion networks and billing schemes.
Explore solutionReal-Time Pre-Payment Screening
Sub-second fraud risk scoring on every claim at adjudication — flagging high-risk claims for review before any payment is released.
Explore solutionAnomaly Detection & Outlier Analysis
Unsupervised ML identifies providers billing outside peer norms for code frequency, service volume, and charge amounts.
Explore solutionDuplicate & Phantom Billing Detection
AI cross-references claim history, patient demographics, and provider records to identify duplicate submissions and services never rendered.
Explore solutionProvider Credentialing Risk Scoring
Continuous monitoring of provider credentials, sanctions, and behavioural patterns to flag high-risk providers before they bill.
Explore solutionSIU Case Management Integration
Automated case creation, evidence packaging, and referral workflows for Special Investigations Unit teams.
Explore solutionFWA Compliance & Reporting
Full compliance with federal and CMS fraud, waste, and abuse programme requirements.
Ready to Stop Paying Fraudulent Claims?
Let us run a retrospective analysis on your claims data. We'll surface the fraud patterns your current system is missing.