FraudLens
Score every claim at FNOL, in milliseconds. Flag suspicious claims before payout with anomaly detection, image forensics, and network analysis.
FraudLens is insurance fraud detection that scores every claim at first notice of loss in milliseconds, using anomaly detection, image forensics and network analysis, and sends the suspicious ones to SIU with the evidence attached.
Who is this for?
SIU leaders and claims executives at mid-market carriers who are tired of being told the enterprise tools need six months and a consultant, and compliance officers who have to explain every AI decision to a regulator. It sits beside your claims system and your existing rules engine rather than replacing either.
What problem does it solve?
Rules catch yesterday's fraud. A rules engine flags the patterns someone already wrote down, which is why organised rings and the quieter forms of exaggeration pass straight through, and why the flag arrives after the check is written.
The photos are the new problem. Generated and edited damage images look convincing to a human adjuster on a busy day, and a ring that spreads across claimants, providers and attorneys is invisible one claim at a time.
How does it work?
Claim data, photos and documents flow in at FNOL and a model assigns a fraud probability in milliseconds. Anomaly detection looks at claim patterns, networks and submission behaviour; image forensics checks compression artifacts, clone and splice marks, lighting and colour inconsistencies, EXIF and GPS metadata, and the fingerprints that generative tools leave behind; network analysis maps the relationships between claimants, providers, attorneys and witnesses to find the ring, not just the claim.
High-risk claims route to SIU with an evidence package and the visual tools to review it; nothing is denied automatically. Investigators deny, recover or refer for prosecution, and the outcome feeds back into the model. Every score carries its reasons, which is what a regulator asks for and what an investigator needs to act.
Thresholds are tunable, precision and recall sit on the KPI dashboard, and the lift is reported honestly: most insurers see detection rates improve 25 to 40 percent with 10 to 20 percent fewer false positives, depending on the baseline fraud rate and claim mix.
Capabilities
- 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)
Outcomes
- Detection 3x more fraud vs. rules-based
- Resolution 50% faster SIU case closure
- Precision 75% fewer false positives
What does it connect to?
- Claims management systems through API adapters, scoring at intake and during investigation
- Fraud bureaus and external databases (ISO, NICB and others), looked up in real time
- SIU investigation workflows and case management
- Outcome tracking that feeds model updates
How is it deployed, and how long does it take?
Scoring runs on your own claims history, with no consortium data sharing required. Explainability is built in because insurance regulators require carriers to explain AI decisions; every flag is documented with its justification.
It covers auto (staged collisions, phantom passengers, inflated repairs), property (arson, inflated losses, pre-dated damage), workers' compensation (provider mills, attorney rings) and liability (staged falls, coordinated witnesses), with the detection methods matched to each line.
Scoring claims within 30 days of the start; deployment complete in 60 to 90 days. SIU case closure typically runs about 50 percent faster once the evidence packages arrive with the referral.
Industry context
Explore the related workflows and other solutions for your industry.