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Not after.","Real-time detection at the point of submission. Continuous learning. Zero impact on clean claims.",[27,31,35,39,43,47],{"title":28,"description":29,"icon":30},"Real-Time Pattern Detection","Detects upcoding, unbundling, phantom billing, and abnormal utilization at the moment of claim submission. Not months later during audits.","i-lucide-scan-search",{"title":32,"description":33,"icon":34},"Live Provider Profiling","Tracks individual clinic behavior in real-time. Flags statistical outliers who consistently over-prescribe or upcode compared to network averages.","i-lucide-user-search",{"title":36,"description":37,"icon":38},"Smart Rule Validation","Automatically flags impossible code combinations, frequency limit breaches, and unbundled services. Blocks waste before payment occurs.","i-lucide-shield-check",{"title":40,"description":41,"icon":42},"Collusion Detection","Identifies suspicious cross-referral patterns, shared member pools, and coordinated billing anomalies across multiple providers.","i-lucide-network",{"title":44,"description":45,"icon":46},"Continuous Learning","AI models learn from new fraud vectors, investigation outcomes, and confirmed cases. Detection accuracy improves over time without manual rule updates.","i-lucide-brain",{"title":48,"description":49,"icon":50},"Investigation Dashboard","AI-generated case summaries with evidence, risk scores, pattern visualizations, and recommended actions. Investigators focus on confirmed high-risk cases.","i-lucide-layout-dashboard",{"eyebrow":52,"title":53,"columns":54,"rows":59},"How We Compare","The only FWA prevention embedded at point of care.",[55,56,57,58],"Legacy TPAs","Manual SIU","AI FWA Scanners","Mazecare",[60,66,70,73,77,80,83,86,89,92,95,98],{"feature":61,"values":62},"Pre-payment FWA blocking",[63,63,64,65],"cross","Partial","check",{"feature":67,"values":68},"Real-time detection at submission",[63,63,69,65],"Limited",{"feature":71,"values":72},"Structured clinical data input",[63,63,63,65],{"feature":74,"values":75},"Upcoding detection",[76,76,65,65],"Reactive",{"feature":78,"values":79},"Unbundling detection",[76,76,65,65],{"feature":81,"values":82},"Phantom billing detection",[63,64,64,65],{"feature":84,"values":85},"Provider collusion detection",[63,64,69,65],{"feature":87,"values":88},"Live provider profiling",[63,64,69,65],{"feature":90,"values":91},"Continuous learning from outcomes",[63,63,65,65],{"feature":93,"values":94},"AI investigation summaries",[63,63,64,65],{"feature":96,"values":97},"Embedded in clinical workflow",[63,63,63,65],{"feature":99,"values":100},"Own AI-native OS",[63,63,63,101],"✓ Mazecare OS",{"title":103,"description":104,"primaryButtonText":105,"secondaryButtonText":106,"secondaryButtonTo":107},"Ready to stop paying for fraud?","See the FWA prevention engine detect live patterns against real claims data.","Book a Demo","API Documentation","mailto:julien@mazecare.com","md","AI MODULE & STANDALONE",{"eyebrow":111,"title":112,"items":113},"FAQ","Frequently asked questions.",[114,117,120,123,126,129],{"question":115,"answer":116},"How is this different from traditional FWA detection?","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.",{"question":118,"answer":119},"What types of fraud does it detect?","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.",{"question":121,"answer":122},"Does it slow down clean claims?","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.",{"question":124,"answer":125},"How does provider profiling work?","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.",{"question":127,"answer":128},"Can it integrate with our existing SIU workflow?","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.",{"question":130,"answer":131},"Does this work outside Asia?","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.",{"eyebrow":133,"title":134,"description":135,"items":136},"Maximum Flexibility","AI Model Agnostic. Your AI, Your Choice.","Choose or bring your own models for fraud detection, anomaly scoring, and pattern recognition.",[137,141,145,149],{"title":138,"description":139,"icon":140},"Pre-integrated Models","Fraud detection and anomaly scoring models optimized for health insurance claims, ready out of the box.","i-lucide-server",{"title":142,"description":143,"icon":144},"Open-source Models","Run open-source anomaly detection within your own infrastructure for full data sovereignty.","i-lucide-git-branch",{"title":146,"description":147,"icon":148},"Custom Fine-tuned","Train on your confirmed fraud cases and regional billing patterns for higher detection accuracy.","i-lucide-settings",{"title":150,"description":151,"icon":152},"Bring Your Own API","Connect any external fraud engine, risk scoring API, or analytics platform. Use existing vendor agreements.","i-lucide-external-link","Prevention.",{"script":155},[156],{"type":157,"key":158,"data-nuxt-schema-org":159,"nodes":160},"application\u002Fld+json","schema-org-graph",true,[161],{"@type":162,"@id":163,"name":164,"description":165,"applicationCategory":166,"operatingSystem":167,"url":168,"featureList":169,"publisher":170},"SoftwareApplication","https:\u002F\u002Fwww.mazecare.com\u002Fproducts\u002Fai-modules\u002Fpayers\u002Ffraud-waste-abuse-prevention#software","Mazecare AI 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.","HealthApplication","Web-based","https:\u002F\u002Fwww.mazecare.com\u002Fproducts\u002Fai-modules\u002Fpayers\u002Ffraud-waste-abuse-prevention",[28,32,36,40,44,48],{"@id":171},"https:\u002F\u002Fwww.mazecare.com\u002F#identity","\u002Fimages\u002Fai-modules\u002Fhero\u002Ffraud-waste-abuse-prevention",{"eyebrow":174,"title":175,"items":176},"Key Impact","Prevent 3 to 10% of claims spend lost to FWA.",[177,182,187,191],{"metric":178,"label":179,"desc":180,"icon":181},"3-10%","Claims spend saved from FWA","Industry average for FWA leakage. Shifts from pay-and-chase to prevent-before-pay, dramatically reducing investigation costs and recovery efforts.","i-lucide-piggy-bank",{"metric":183,"label":184,"desc":185,"icon":186},"Real-time","Detection at submission","Suspicious activity flagged at the moment of claim submission. Honest providers are no longer penalized by slow blanket audits. Clean claims flow faster.","i-lucide-zap",{"metric":188,"label":189,"desc":190,"icon":46},"Continuous","Learning from new vectors","AI models improve with every investigation outcome and confirmed case. Detection accuracy compounds over time without manual rule updates.",{"metric":192,"label":193,"desc":194,"icon":195},"Asia-built","Globally exportable","Built 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.","i-lucide-globe",{"eyebrow":197,"title":198,"items":199},"Integration","Plug into your SIU or run natively.",[200,203],{"title":201,"desc":202,"icon":152},"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.",{"title":204,"desc":205,"icon":206},"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.","i-lucide-layers",{},"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.","Fraud, Waste & Abuse","\u002Fproducts\u002Fai-modules\u002Fpayers\u002Ffraud-waste-abuse-prevention","index, follow",[213],{"@type":162,"@id":163,"name":164,"description":165,"applicationCategory":166,"operatingSystem":167,"url":168,"featureList":214,"publisher":215},[28,32,36,40,44,48],{"@id":171},{"title":217,"description":218,"robots":211},"AI Fraud, Waste & Abuse Prevention for Payers | Mazecare","AI catches upcoding, unbundling, phantom billing and collusion in real time — flagging suspicious claims at submission, not months after payment.",{"loc":210,"lastmod":220},"2026-04-09","products\u002Fai-modules\u002Fpayers\u002Ffraud-waste-abuse-prevention",{"eyebrow":223,"title":224,"items":225},"How It Works","From submission to verdict in milliseconds.",[226,230,234,238],{"label":227,"title":228,"desc":229},"Step 01","Claim Submitted","Claim arrives with structured clinical data from the provider OS.",{"label":231,"title":232,"desc":233},"Step 02","AI Scans","Rules engine and AI models check for FWA patterns in real-time.",{"label":235,"title":236,"desc":237},"Step 03","Risk Scored","Each claim receives a risk score. High-risk claims are blocked or flagged instantly.",{"label":239,"title":240,"desc":241},"Step 04","Resolved","Clean claims flow through. Flagged claims go to investigators with AI evidence packs.","pGPMbsp56IaTIfCAT47pd172B5ftsu3AnvBGxWuGeT8",1776840851053]