Health insurer boosts fraud detection by 47% with AI-powered claims analytics

The health plan set out to upgrade its fraud detection capability with AI-driven anomaly detection and network analytics—improving both catch rate and investigator efficiency without increasing member or provider friction. Success meant surfacing complex schemes earlier, predicting emergent patterns, and prioritizing cases for maximum recovery. The system needed to deploy anomaly detection to elevate SIU hit rates, use network and graph analytics to expose collusion rings, score claims pre-payment to prevent improper payouts, and optimize investigation queues for ROI and compliance.

Legacy rules caught only a subset of sophisticated schemes, investigations moved slowly, and prior ML attempts overwhelmed the SIU with false positives. Meanwhile, organized provider networks exploited real-time gaps to push fraudulent claims through payment:

• Limited detection: Rule-based systems detected just 34% of confirmed schemes
• Slow investigations: Average investigation cycle at 45 days limited throughput
• False positive overload: Earlier ML models produced 73% false positives causing analyst fatigue
• Network blindness: Provider networks undetected in 67% of schemes (≈$180M/yr loss)
• Payment timing gaps: 58% of fraudulent claims were paid before detection
• Competitive disadvantage: Competitors achieved 2.5× higher recoveries via advanced analytics

Standard fraud detection tools couldn't adapt to sophisticated organized schemes or provide the precision needed to avoid overwhelming investigators with false alarms. The company needed expert-trained models that could identify complex patterns while maintaining investigation efficiency.

CleverX mobilized a blended team of certified fraud examiners, clinical analysts, and data scientists to build a prevention-first analytics stack—pairing high-precision models with legally defensible workflows and SIU-friendly tooling.

Expert recruitment:

• 21 specialists: 9 CFEs, 7 clinical analysts, 5 data scientists
• Average 9 years in healthcare fraud with law-enforcement backgrounds
• Expertise across provider/member fraud and organized crime schemes
• Deep familiarity with CMS regulations and False Claims Act requirements

Technical framework:

• Anomaly detection across 500 fraud indicators for comprehensive coverage
• Graph/network analysis to reveal collusion patterns and organized schemes
• Pre-payment risk scoring integrated with claims adjudication systems
• Case management triage to prioritize high-yield leads for investigation

Quality protocols:

• Investigation playbooks meeting legal standards of proof for prosecution
• Evidence handling with chain-of-custody preservation for court cases
• Clinical review layer to reduce false positives and improve precision
• CMS-compliant reporting for program integrity and regulatory requirements

The rollout followed staged sprints from intelligence gathering to SIU enablement, ensuring measurable gains at each step and fast learning loops with investigators.

Weeks 1-2: Fraud pattern analysis and data mining

• Analyzed 5 years of cases to catalog schemes and indicators
• Discovered $25M in previously undetected fraud patterns
• Mapped provider networks to expose collusion clusters
• Built comprehensive fraud taxonomy across all product lines

Weeks 3-6: Model development and validation

• Built ensemble models covering 15 fraud types with high precision
• Implemented graph analytics finding networked behavior patterns
• Risk-scored 150M claims for pre- and post-pay review prioritization
• Validated models against known fraud cases and false positive rates

Weeks 7-8: System deployment and integration

• Integrated real-time scoring into claims processing workflows
• Deployed workflows to 85 SIU analysts with training and support
• Launched provider monitoring dashboards for continuous surveillance
• Established automated alerts and escalation procedures

Weeks 9-10: Investigation optimization and training

• Prioritized 2,000 high-risk cases for immediate action
• Trained SIU on new tooling and triage methods for efficiency
• Established KPIs and continuous-improvement cadence
• Created feedback loops for model refinement and accuracy improvement

CleverX's fraud detection transformation delivered comprehensive improvements across investigation efficiency, detection quality, and financial performance.

Efficiency gains:

Cycle time shrank and analyst productivity climbed without expanding headcount. Investigation cycle time was cut from 45 to 21 days, investigator productivity increased by 52%, alert generation moved from monthly to daily, and pre-payment detection prevented 38% more improper payments.

Quality improvements:

Detection got sharper while noise declined. The system achieved 47% increase in fraud detection rate, reduced false positives from 73% to 41%, identified 8 new scheme archetypes, and network detection surfaced 31% more conspirators.

Business impact:

Recoveries rose while leakage and costs fell. The plan achieved $4.7M/month additional recoveries, prevented $38M in losses through pre-payment detection, reduced investigation costs by $2.1M via prioritization, and avoided $5.2M in regulatory penalties.

Strategic advantages:

The initiative established a durable, defensible anti-fraud capability including a fraud intelligence database with 50,000 validated cases, formal information-sharing pathways with law enforcement, early-warning analytics for emerging schemes, and platform extension to support value-based contracts.

The health plan's anti-fraud program received recognition from the National Health Care Anti-Fraud Association for excellence in AI-driven fraud detection and prevention.