Reserve accuracy is one of the most important and hardest-to-get-right numbers in insurance -- and AI is improving the odds.
An initial reserve set too low requires a painful increase later, triggering adverse development. A reserve set too high ties up capital and distorts loss ratio reporting. The challenge is that at the time of initial reserve setting, critical facts about a claim are often still unknown.
AI-assisted reserving tools analyze available case data -- injury type, jurisdiction, attorney involvement, treatment patterns -- and compare the emerging claim profile against a database of similar closed claims to recommend initial and revised reserve levels with documented rationale.
The result is more consistent reserving across an adjuster team, faster identification of claims requiring immediate reserve adequacy review, and better audit trails for reserve decisions that regulators and auditors increasingly examine.
#ClaimsReserving #ReserveAdequacy #ClaimsManagement #InsuranceTech #PandCInsurance
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