Life Insurance: Fraud, Waste & Abuse Detection
A regional insurer needed AI-level fraud detection without AI-level costs. The Trust Cascade delivered 94% detection at 71% lower cost per claim.
The math didn't work
The insurer's fraud detection system was a patchwork of rules and ML models. A recent LLM POC showed promise — but projected costs made it unaffordable at scale.
Pure rules
58% detection, low cost — too many misses
Rules + ML
71% detection, acceptable cost — too many false positives (40%)
Rules + ML + LLM
89% detection — unaffordable at scale ($540K/year)
Operational problems
- Investigators overwhelmed by false positives
- Sophisticated fraud schemes slipping through
- No explanation for why claims were flagged
- Fraud team couldn't justify AI investment to CFO
The business case gap
- Estimated $12M annual fraud losses
- LLM solution would cost $540K/year
- CFO demanded 10:1 ROI
- Solution needed to cost less than $120K/year
Trust Cascade implementation
Analysis
- Analyzed 18 months of claims data
- Mapped existing detection rules and ML models
- Identified fraud pattern categories by complexity
- Modeled economic value of detection by claim type
Cascade Design
- Designed 5-level cascade architecture
- Defined routing logic based on claim value and confidence
- Created ROI-based escalation thresholds
- Designed APLS (self-learning rules) feedback loop
Implementation
- Integrated existing rules engine as Level 1
- Retrained ML models for Level 2 with new features
- Deployed single-agent analysis for Level 3
- Built multi-agent debate system for Levels 4-5
Optimization
- Tuned routing thresholds based on production data
- Activated APLS for automatic rule generation
- Deployed Red Queen adversarial testing
- Trained investigation team on new workflows
Intelligent Trust Cascade
| Level | Layer | Function | Cost/Claim | Volume |
|---|---|---|---|---|
| 1 | Rules Engine | Known patterns, velocity checks | $0.0001 | 68% |
| 2 | ML Models | Anomaly scoring, risk classification | $0.001 | 22% |
| 3 | Single Agent | Complex pattern analysis | $0.008 | 7% |
| 4 | Agent Panel | Multi-perspective review | $0.025 | 2% |
| 5 | Adversarial Debate | Prosecution vs defense | $0.045 | 1% |
ROI-based routing
- Claims <$1K: Max Level 2 (not worth AI cost)
- Claims $1K-$10K: Max Level 3 (single agent sufficient)
- Claims $10K-$50K: Max Level 4 (panel review justified)
- Claims >$50K: Full cascade (adversarial debate)
Self-learning rules (APLS)
- When Levels 3-5 catch fraud, system extracts pattern
- Generates candidate rule for Level 1 or Level 2
- Human review and approval workflow
- Automatic deployment to lower levels
AI accuracy at rule-level cost
| Metric | Before | After | Change |
|---|---|---|---|
| Detection rate | 71% | 94% | +32% |
| False positive rate | 40% | 12% | -70% |
| Cost per claim (at 2M claims) | $0.008 | $0.0023 | -71% |
| Annual detection cost | $180K | $55K | -69% |
| Fraud prevented (estimated) | $8.5M | $11.3M | +$2.8M |
Additional outcomes
- CFO approved expansion to health claims
- Investigation team productivity up 3x
- 127 new rules auto-generated in first 6 months
- System improving monthly (detection migrating to cheaper levels)
"Everyone told us we needed AI for fraud detection. Nobody told us we'd go bankrupt running it at scale. Rotascale showed us how to get AI-level accuracy at rule-level cost. The cascade paid for itself in the first quarter."
— Chief Risk Officer
Similar case studies
Facing similar challenges?
Let's discuss how the Trust Cascade can deliver AI-level detection at affordable cost.