Claims Modelling
We build the models that predict which claims will develop into large losses, which will settle quickly and which need specialist handling, and attach the drivers behind every prediction.
How a claim is predicted, triaged and settled, and what the board sees about frequency, severity and development.
We build the models that predict which claims will develop into large losses, which will settle quickly and which need specialist handling, and attach the drivers behind every prediction.
We design the triage, allocation and settlement strategy that routes each claim to the right handler and the right intervention, including the customers in vulnerable circumstances.
We build live dashboards reporting claims frequency, severity, settlement time and reserve development to the board.
Claims analytics pays where it changes the handling decision, so ask what happens to the claim after the score. At Gini we build predictive loss models that flag the claim likely to develop, fraud network analysis that finds the related cluster rather than the single suspicious claim, automated extraction from claim documents, and the analytics that identify subrogation recovery worth pursuing.
That is the design intent. The model Gini builds scores the claim at notification, using its own features and its relationships to prior claims, and puts the flag in front of a handler while the payment decision is still open. Post-payment detection recovers less and costs more, which is why the timing matters more than the model’s headline accuracy.
At Gini we use it to find the relationships a claim-level score is blind to: shared addresses, bank details, vehicles, garages and contact points linking claims that look unremarkable individually. Organised claims fraud is a network before it is a loss, and the cluster is visible in the graph earlier than in any single score.
Yes. Gini extracts from correspondence, invoices, reports and scanned records into a standardised queryable format your models can use directly, with a confidence score flagging what needs human review. The point is to make the document’s contents available to the model rather than to remove the handler.