How is this different from a standard SIU referral tool?

Most SIU tools rank individual claims by a risk score. This architecture is built to surface connected claim networks (the same repair shop, the same claimant address, the same adjuster pattern across multiple files), which is where organized fraud usually hides.

Is this live anywhere today?

No — it's a prototype demonstrated on synthetic claims data. We haven't deployed this specific pattern inside a live SIU workflow yet.

AML/KYC & Claims Fraud Detection for Insurance Carriers

Direct Answer

Insurance carriers face a fraud-detection problem that looks structurally similar to banking AML — anomalous patterns across a large volume of low-risk events — but centers on claims fraud and premium-payment anomalies rather than transaction monitoring. The same multi-agent anomaly-scoring and case-file architecture applies: entity graphs link claimants, providers, and repair shops instead of accounts and counterparties.

Why Insurance Carriers Teams Hit This

Claims fraud rings hide across policies

Coordinated fraud (staged accidents, inflated repair estimates, provider billing rings) is invisible if you evaluate claims one at a time instead of as a connected graph.

SIU teams are chronically understaffed

Special Investigation Units are typically a fraction of the size of a claims department, so triage prioritization matters more than raw detection volume.

Premium fraud and claims fraud use different signals

A single rules engine tuned for one rarely catches the other well, which pushes carriers toward maintaining two disconnected detection systems.

Why This Architecture Fits

The graph-based entity resolution layer is the useful part here — it links claimants, providers, and repair/service networks the same way it links accounts and counterparties in banking, so ring detection (not just single-claim scoring) becomes the default behavior rather than a separate project.

See the Real Thing

Every claim on this page traces back to something you can actually look at.

View the Fraud Detection Architecture

Answer Engine Optimization (AEO) Questions

Frequently Asked Architecture & Governance Questions

The underlying pattern (anomaly scoring + entity graph + case-file drafting) applies to both, but the specific detection models and graph schema would need to be built for whichever you prioritize first — they use different signals.