Fraud, Waste & Abuse
Prevention.
AI monitors claims in real-time for upcoding, unbundling, phantom billing, collusion, and abnormal utilization. Flags suspicious activity at submission — not months later. The only provider-side OS with real-time claims in the clinical workflow, enabling pre-payment FWA prevention.
Block fraud before you pay. Not after.
Real-time detection at the point of submission. Continuous learning. Zero impact on clean claims.
Real-Time Pattern Detection
Detects upcoding, unbundling, phantom billing, and abnormal utilization at the moment of claim submission. Not months later during audits.
Live Provider Profiling
Tracks individual clinic behavior in real-time. Flags statistical outliers who consistently over-prescribe or upcode compared to network averages.
Smart Rule Validation
Automatically flags impossible code combinations, frequency limit breaches, and unbundled services. Blocks waste before payment occurs.
Collusion Detection
Identifies suspicious cross-referral patterns, shared member pools, and coordinated billing anomalies across multiple providers.
Continuous Learning
AI models learn from new fraud vectors, investigation outcomes, and confirmed cases. Detection accuracy improves over time without manual rule updates.
Investigation Dashboard
AI-generated case summaries with evidence, risk scores, pattern visualizations, and recommended actions. Investigators focus on confirmed high-risk cases.
From submission to verdict in milliseconds.
Prevent 3 to 10% of claims spend lost to FWA.
3-10%
Claims spend saved from FWAIndustry average for FWA leakage. Shifts from pay-and-chase to prevent-before-pay, dramatically reducing investigation costs and recovery efforts.
Real-time
Detection at submissionSuspicious activity flagged at the moment of claim submission. Honest providers are no longer penalized by slow blanket audits. Clean claims flow faster.
Continuous
Learning from new vectorsAI models improve with every investigation outcome and confirmed case. Detection accuracy compounds over time without manual rule updates.
Asia-built
Globally exportableBuilt where the gap is largest — most Asian markets still rely on manual, retrospective FWA review. Adapts to local billing codes and regulatory frameworks in any geography.
Plug into your SIU or run natively.
Standalone via Open API
Submit claims for FWA screening via API. Receive risk scores and evidence packs. Export flagged cases to your SIU or investigation platform.
Native on Mazecare Insurance OS
FWA checks run in parallel with adjudication. Fraud blocked before payment. Investigation dashboard built in. End-to-end from detection to resolution.
AI Model Agnostic. Your AI, Your Choice.
Choose or bring your own models for fraud detection, anomaly scoring, and pattern recognition.
Pre-integrated Models
Fraud detection and anomaly scoring models optimized for health insurance claims, ready out of the box.
Open-source Models
Run open-source anomaly detection within your own infrastructure for full data sovereignty.
Custom Fine-tuned
Train on your confirmed fraud cases and regional billing patterns for higher detection accuracy.
Bring Your Own API
Connect any external fraud engine, risk scoring API, or analytics platform. Use existing vendor agreements.
The only FWA prevention embedded at point of care.
| Feature | Legacy TPAs | Manual SIU | AI FWA Scanners |  |
|---|---|---|---|---|
| Pre-payment FWA blocking | Partial | |||
| Real-time detection at submission | Limited | |||
| Structured clinical data input | ||||
| Upcoding detection | Reactive | Reactive | ||
| Unbundling detection | Reactive | Reactive | ||
| Phantom billing detection | Partial | Partial | ||
| Provider collusion detection | Partial | Limited | ||
| Live provider profiling | Partial | Limited | ||
| Continuous learning from outcomes | ||||
| AI investigation summaries | Partial | |||
| Embedded in clinical workflow | ||||
| Own AI-native OS | ✓ Mazecare OS |
Frequently asked questions.
Traditional FWA tools are retrospective. They scan claims after payment, then chase recoveries. Mazecare detects and blocks FWA at the moment of submission, before payment occurs. This shifts the model from pay-and-chase to prevent-before-pay.
Upcoding, unbundling, phantom billing, abnormal utilization, frequency breaches, impossible code combinations, provider collusion, and cross-referral loops. AI continuously learns new fraud vectors from investigation outcomes.
No. FWA checks run in parallel with adjudication in milliseconds. Clean claims are unaffected. Only high-risk claims are blocked or flagged. Honest providers benefit from faster payment cycles.
AI tracks billing patterns per clinic in real-time and compares against network averages. Statistical outliers are flagged with evidence. This replaces slow, blanket audits with targeted, data-driven investigation.
Yes. Flagged cases export to your Special Investigation Unit with AI evidence packs via API. Or use the built-in investigation dashboard for end-to-end case management.
Yes. The engine is built for any market. It adapts to local billing codes, regulatory frameworks, and fraud patterns. Built in Asia where the gap is largest, but fully exportable to any geography.
Ready to stop paying for fraud?
See the FWA prevention engine detect live patterns against real claims data.