Claims analytics

Claims analytics applies data analysis to the insurance claims process — from first notice of loss through investigation, adjustment, and payment — to speed handling, control cost, and catch problems that manual review would miss. It draws on structured claims data, adjuster notes, and increasingly images, telematics, and third-party records.

Common applications include triage models that route straightforward claims to fast, automated settlement while flagging complex or high-value ones for a human adjuster; severity prediction, which estimates how much a claim will ultimately cost while it's still open; and fraud detection models that score claims for suspicious patterns, such as staged accidents or exaggerated damage. This differs from underwriting analytics, which prices risk before a policy is written; claims analytics operates after a loss has already occurred.

Claims analytics matters because claims payments are an insurer's largest cost and a major driver of both the loss ratio and combined ratio, so even small improvements in accuracy or speed have an outsized effect on profitability and customer experience. It is a core input to actuarial modeling used for reserving, since open claims must be estimated before they are fully paid. A common pitfall is training severity or fraud models on historical claims data that reflects past investigation practices, which can encode and repeat prior biases in who gets flagged for scrutiny.