Use Cases — Fraud rings, claims, identity, risk networks

Use cases

Real insurance work, solved with links

Five flagship use cases for fraud, claims, identity and underwriting.

Spot fraud networks

Challenge: Organised groups work across many claims, people and providers and slip past one-claim-at-a-time reviews.
How we do it: We build the network of people involved and spot suspicious groups and shared signals.
What you get: A case file per network with people, events and a clear explanation of the links.
Result: Far more organised fraud found and faster action.

Group claims

Challenge: Coordinated behaviour gets missed when claims are reviewed one by one.
How we do it: We group claims by shared people, locations, vehicles and patterns.
What you get: Prioritised groups with risk score, explanation and recommended next step.
Result: Less loss, smarter triage and more focused investigation.

Match identities

Challenge: Duplicate and conflicting records disrupt risk and investigation decisions.
How we do it: Smart rules combined with probability matching across people, companies, vehicles and assets.
What you get: One trusted profile per person or company, fully traceable.
Result: Cleaner data, better analysis, fewer false alarms downstream.

Review providers

Challenge: Some garages, clinics or intermediaries quietly charge too much or too often.
How we do it: We compare providers to their peers and to the normal pattern.
What you get: Provider risk profiles with peer comparison and clear explanation.
Result: Targeted reviews, lower claim costs, healthier provider network.

See risk clearly

Challenge: Underwriting and portfolio teams lack a connected view of accumulated risk.
How we do it: We map risk across linked people, regions and lines of business.
What you get: An interactive overview of where risk is building up.
Result: Better selection, smarter accumulation control and better reinsurance.

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