FraudLens — Insurance fraud detection
Score every claim at FNOL — in milliseconds. Flag suspicious claims before payout with anomaly detection, image forensics, and network analysis.
Enterprise fraud detection at mid-market speed. While legacy vendors take 6-12 months to deploy, FraudLens is scoring claims in 30 days. No consultants. No consortium data sharing. Just AI that finds fraud your rules-based system misses.
- Detection 3x more fraud vs. rules-based
- Resolution 50% faster SIU case closure
- Precision 75% fewer false positives
Outcomes
FraudLens vs. Legacy Detection
| Capability | FraudLens | Rules-Based Systems | Manual SIU Review |
|---|---|---|---|
| Detection Timing | Real-time at FNOL | Batch processing | Post-payment |
| New Fraud Patterns | ✓ ML adapts automatically | ✗ Rules must be updated | ✗ Experience-dependent |
| Organized Rings | ✓ Network analysis | Limited visibility | Time-intensive |
| AI-Generated Photos | ✓ Forensics detection | ✗ Not detectable | ✗ Hard to spot |
| False Positive Rate | Low (ML optimized) | High (broad rules) | Varies by analyst |
| Scalability | Unlimited claims | Rules explosion | Headcount-limited |
| Explainability | ✓ Evidence packages | Rule triggered | Analyst judgment |
Technical Specifications
What it does
- Anomaly detection on claim patterns, networks, and submission behavior
- Image Forensics for the AI Era — Fraudsters now use GANs, DALL·E, and Midjourney to create fake damage photos. FraudLens detects what human eyes miss with pixel-level analysis, AI-generation detection, metadata forensics, and cross-claim matching
- Fraud Ring Detection — Organized fraud costs more than opportunistic fraud. FraudLens maps entity relationships, detects patterns across providers/attorneys/claimants, and provides visual network graphs for SIU investigation
- SIU workflow integration with investigation queue
- External database lookups (ISO, NICB, fraud bureaus)
How it works
Ingest
Claim data, photos, documents flow in at FNOL
Score
ML model assigns fraud probability in milliseconds
Analyze
Anomaly detection, image forensics, network mapping
Flag
High-risk claims routed to SIU with evidence packages
Investigate
SIU reviews with visual tools and justification
Resolve
Deny, recover, or refer for prosecution
Ingest
Claim data, photos, documents flow in at FNOL
Score
ML model assigns fraud probability in milliseconds
Analyze
Anomaly detection, image forensics, network mapping
Flag
High-risk claims routed to SIU with evidence packages
Investigate
SIU reviews with visual tools and justification
Resolve
Deny, recover, or refer for prosecution
Fraud Detection by Line of Business
Auto Claims
Property Claims
Workers' Compensation
General Liability
What you get
- Fraud scoring model tuned to your claim types
- Investigation queue with evidence packages
- API adapters for SIU systems and external databases
- KPI dashboard: detection rate, precision, investigation cycle time
- Quarterly model retraining with new fraud patterns
Deployments & integrations
- Cloud or on-prem
- API integration with claims platforms and SIU systems
- Real-time scoring at FNOL
Security & governance
- SOC 2 Type II audited
- RBAC with SIU/fraud analyst/admin roles
- Audit trail for all scoring decisions
- Privacy-preserving analytics (no PII leakage)
Related
Products
Services
Industries
Frequently Asked Questions
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