Fraudulent claims identified before the payout is approved
QAble's AI fraud detection solution analyses claim submissions in real time — scoring risk across identity, treatment, network, and behavioural signals so adjusters investigate what matters and approvals flow on clean claims.
Engineering teams that rely on QAble
Why rule-based fraud detection fails as fraud grows more sophisticated
Static rule libraries, manual review bottlenecks, and post-payment detection create a fraud exposure window that organised networks exploit systematically.
Where legacy fraud detection fails:
See how AI detection performs on your claims data.
QAble AI scores every claim in real time — before payment, not after.
Detection at submission means fraud is caught when intervention is possible — not discovered months after the payout when recovery costs exceed the original loss.
Fraud Detection Rate
Percentage of fraudulent claims correctly identified and flagged before payment approval in live processing.
False Positive Rate
Proportion of legitimate claims incorrectly flagged — directly impacting adjuster workload and claimant experience.
Detection Latency
Time from claim submission to fraud risk score generation — measured against straight-through processing SLA targets.
Investigation ROI
Fraudulent value recovered versus cost of investigation — the primary financial efficiency metric for fraud operations.
Fraud patterns the AI solution detects
QAble's detection models cover the full fraud spectrum — from individual identity manipulation to coordinated organised fraud ring activity.
Identity & Document Fraud Detection
Detection of fabricated identities, forged supporting documents, and synthetic policy applications — using document authenticity signals and identity consistency analysis across claim submissions.
Medical & Treatment Pattern Analysis
Analysis of medical claim submissions for treatment inflation, unbundling, upcoding, and treatment patterns inconsistent with injury severity — validated against clinical coding benchmarks.
Staged Accident & Collision Detection
Identification of staged accident patterns in vehicle and liability claims — using accident circumstance analysis, claimant network overlap, repair cost anomalies, and reporting timing signals.
Provider & Claimant Network Analysis
Graph-based network analysis identifying coordinated fraud rings — detecting clusters of claimants, providers, attorneys, and repair facilities with statistically anomalous co-occurrence patterns.
Behavioural Anomaly Detection
Detection of behavioural signals inconsistent with genuine claims — submission timing anomalies, policy coverage awareness indicators, claim escalation patterns, and communication behaviour flags.
Organised Fraud Ring Detection
AI-driven detection of coordinated organised fraud across multiple claims and policy periods — identifying rings that operate below individual claim thresholds but represent significant aggregate loss exposure.
QAble Fraud Detection Deployment Process
A structured data assessment, model configuration, and integration process that goes from claims data to live fraud scoring without replacing your existing workflow.
Claims Data Assessment & Integration
QAble assesses your claims data sources, submission formats, and existing workflow systems — mapping the data available for fraud pattern modelling and designing the integration architecture before configuration begins.
Fraud Pattern Modelling & Baseline Configuration
The AI detection models are configured against your historical claims data — establishing fraud pattern baselines, tuning sensitivity thresholds, and validating detection accuracy on known fraud cases before live deployment.
Model Validation & Accuracy Testing
Detection accuracy is validated across fraud categories — measuring true positive rates, false positive rates, and detection latency against your claims volume to ensure the model meets operational thresholds before go-live.
Production Integration & Workflow Setup
The fraud detection solution is integrated into your claims management workflow — configuring risk score delivery, investigation queue routing, adjuster alert thresholds, and audit trail generation for compliance requirements.
Live Monitoring & Continuous Model Improvement
Post-deployment, QAble monitors model performance, tracks emerging fraud patterns, and updates detection capabilities as fraud behaviours evolve — keeping detection accuracy ahead of new evasion tactics.
What the solution delivers
Four integrated components — fraud scoring, workflow integration, compliance audit trails, and performance monitoring — deployed as a single solution.
Fraud Risk Scoring Engine
Investigation Workflow Integration
Audit Trail & Compliance Reporting
Model Performance Dashboard
Fraud patterns QAble AI detects and prevents
These organised fraud patterns represent the primary loss exposure for insurers relying on rule-based detection — each exploiting a systematic gap that AI pattern analysis closes.
Identity Fabrication & Document Forgery
Fabricated policyholder identities and forged supporting documents submitted with otherwise plausible claim details pass manual review at volume — each approved claim establishing a payment history that enables repeat fraud under the same synthetic identity.
Medical Treatment Inflation Networks
Coordinated networks of claimants and complicit providers systematically inflate treatment records — billing for procedures not performed, upcoding injury severity, and unbundling CPT codes to maximise claim value while maintaining individual claims below investigation thresholds.
Staged Accident Rings
Organised groups stage vehicle accidents and deliberately create collision circumstances to generate liability and personal injury claims — using known repair facilities and legal referral networks that create detectable co-occurrence patterns invisible to individual claim review.
Provider-Claimant Collusion
Medical providers, repair facilities, and claimants operating in coordinated referral relationships generate claims that individually appear legitimate but represent structured fraud when analysed across the network — loss exposure that rule-based systems miss entirely.
Policy Stacking & Duplicate Claims
Claimants submitting the same loss event across multiple carriers, or staging sequential claims under multiple policy periods, exploit claims processing silos — payable losses that are only visible through cross-carrier or cross-period data correlation.
Claim Timing & Submission Anomalies
Claims submitted immediately after policy inception, around renewal windows, or with submission patterns statistically inconsistent with genuine loss events represent elevated risk — timing signals that manual review lacks the volume capacity to systematically identify.
Ways to work with QAble
4–8 Weeks
Fraud Detection Pilot
A proof-of-concept deployment on your historical claims data — validating fraud detection accuracy, false positive rates, and ROI potential before full production commitment.
Deliverables
Best for
3–6 Months
Full Fraud Detection Deployment
End-to-end deployment of the AI fraud detection solution — claims data integration, model configuration, workflow setup, adjuster tooling, and compliance audit trail implementation.
Deliverables
Best for
Ongoing
Model Integration & API Access
API-based access to QAble's fraud detection models — integrating fraud risk scores into your existing claims management system with ongoing model updates and performance monitoring.
Deliverables
Best for
Why choose QAble
QAble brings insurance-specific fraud pattern expertise to AI detection — so the models understand how claims fraud actually works, not just how data anomalies look.
QAble Fraud Detection Model Capabilities
Frequently asked questions
Common questions about QAble's AI claim fraud detection solution.
What types of insurance claims does the solution cover?
QAble's AI fraud detection solution covers personal lines and commercial claims across motor (vehicle damage and personal injury), property, medical and health, liability, and workers' compensation. Detection models are configured to your specific claim type distribution, product mix, and historical fraud profile — not applied as generic templates.
How does the AI model avoid flagging legitimate claims as fraud?
False positive management is central to QAble's model configuration process. During the pilot and baseline phase, sensitivity thresholds are tuned against your historical claims population — balancing detection rate against false positive tolerance. The model outputs a risk score with contributing factors rather than a binary flag, enabling adjusters to make informed investigation decisions rather than automatically suspending claims.
How does the solution integrate with existing claims management systems?
QAble's fraud detection solution integrates via API into existing claims management platforms — delivering fraud risk scores and contributing factor data into the claims record without requiring platform replacement. Integration is available with major CMS platforms. For teams without API connectivity, QAble supports batch scoring workflows against claim export files with results returned to the claims workflow.
What audit trail and explainability does the solution provide for regulatory compliance?
Every fraud risk score generated by QAble's solution includes a full decision log — the fraud signals detected, their individual contribution weights, the model version applied, and the timestamp of assessment. This audit trail supports regulatory reporting requirements, internal compliance review, and insurer defence in disputed claim decisions. Explainability output is available in structured data format compatible with standard regulatory reporting workflows.
Stop paying fraud you could have caught at submission
QAble AI scores claims in real time across identity, treatment, network, and behavioural dimensions — flagging high-risk submissions before approval so your adjusters investigate what matters and clean claims flow through.
Fraud detection that works before the money leaves
QAble AI analyses every claim submission in real time — scoring fraud risk across six detection dimensions so your team investigates the right claims and approvals flow on clean submissions.
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See QAble AI fraud detection on your claims data.
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