Link the entities
Claimants, vehicles, providers, addresses and bank details, connected across claims and across time.
/ Risk & compliance /
Claims fraud does not look wrong one claim at a time.
AI that reads patterns across claims, claimants and repair networks — staged losses, coordinated rings and quiet repeat behaviour — and flags them while the file is still open, with the evidence a special investigations unit can act on.
The problem
The single claim looks fine. The pattern does not.
-
Rules catch yesterday's fraud
Static thresholds are learned and worked around. The behaviour moves; the rules do not.
-
The signal lives between claims, not inside one
The same garage, the same medical provider, the same three claimants in different combinations. No single file shows it.
-
False positives train handlers to ignore alerts
When most flags are noise, the flag stops meaning anything and the real ones go with it.
-
By the time it is proven, the payment has cleared
Investigation that starts after settlement recovers a fraction of what prevention would have held.
How it works
Patterns across the portfolio, flagged while the file is open
Score the network, not the form
A claim is scored on the company it keeps: repeat combinations, unusual proximity, timing that does not fit.
Hand the investigator a case
Not an alert — a file with the links drawn, the anomalies named and the evidence assembled.
Investigators receive a case with the network already drawn.
Claims fraud does not look wrong one claim at a time.
What's included
What you get when you run this with Thinkia
A detection layer over the claims portfolio that finds coordinated behaviour and hands the SIU something it can work.
Entity resolution
One claimant across spellings, addresses and policies — the join that makes the rest possible.
Network analysis
Rings, repeat provider clusters and staged-loss structures surfaced across the book.
Anomaly scoring
Deviations from the expected pattern for that claim type, line and geography.
SIU case assembly
The links, the timeline and the documents, packaged for investigation rather than triage.
Feedback loop
Confirmed and dismissed cases retrain the model, so precision improves instead of drifting.
Audit trail
Every flag explains itself — which matters when a decision is challenged.
Powered by Synapse
Results
What changes when this runs in production
Results vary by context, data maturity, and scope. We scope honestly before we promise precisely.
2×
Fraud identified before settlement vs. rules-based screening
Orientative — confirmed in discovery; depends on the starting point.
–50%
False positives reaching claims handlers
Orientative — confirmed in discovery; depends on the starting point.
Weeks earlier
Detection of coordinated activity across the portfolio
Orientative — confirmed in discovery; depends on the starting point.
How we work
From first insight to production
Assess
Assess
Week 1–2
Review current screening, historical confirmed fraud, and what data can be linked.
Design
Design
Week 3–5
Define entity resolution, the network model, scoring thresholds and the SIU handover.
Build
Build
Week 6–9
Run against historical claims to calibrate, then in parallel on live volume.
Govern & scale
Govern & scale
Week 10+
Extend lines of business, close the feedback loop, and monitor precision over time.
Timelines vary by scope and context.
Go deeper
Risk, governance, and what to explore next
Ideas, trends, and tools to stay ahead
Get started
Shall we look at what the pattern shows?
A short session on your book: what is screened today, what the historical cases have in common, and what a network view would surface.