AI for Insurance Recovery: Matching Policy Terms to Covered Losses

Insurance recovery work requires matching the facts of a loss against the terms of potentially dozens of insurance policies. For companies with complex insurance programs, the analysis involves primary and excess policies, specialty lines like cyber and D&O, and possibly historical policies from prior years. Finding all available coverage for a significant loss is detective work that AI makes more thorough.

The Coverage Identification Challenge

Large commercial policyholders often have insurance programs with multiple coverage lines: CGL, property, excess, umbrella, D&O, E&O, cyber, environmental, and others. A significant loss event might trigger coverage under several of these policies, but identifying which policies respond requires analyzing the loss facts against each policy's insuring agreements and exclusions.

Attorneys who handle insurance recovery know that coverage is often found in unexpected places. A cyber incident might trigger not just the cyber policy but also the CGL policy's personal and advertising injury coverage. A products liability loss might implicate historical occurrence-based policies going back decades. Missing an available policy means leaving money on the table for the client.

How AI Finds Coverage

Loss-to-policy matching. AI analyzes the facts of the loss and compares them against the coverage terms of every policy in the client's insurance program. It identifies which policies potentially cover the loss based on the insuring agreement language, regardless of whether the policy is an obvious match. This systematic analysis ensures that no potentially applicable coverage is overlooked.

Historical policy search. For long-tail claims, AI can search through historical policy records to identify policies from prior years that may be triggered by the loss. Many companies have incomplete records of their historical insurance programs. AI can help reconstruct the coverage history by analyzing available documents, broker records, and premium payment records.

Stacking and allocation analysis. When multiple policies are triggered, the allocation of the loss among those policies depends on the applicable jurisdiction's allocation rules. AI can model different allocation approaches, calculating the recovery available to the policyholder under each method and identifying the approach that maximizes recovery.

Tender preparation. AI can help prepare coverage notices and claim tenders for each insurer, ensuring that each notice complies with the specific notice requirements in the applicable policy and is sent within the required timeframes.

Practical Value

Insurance recovery attorneys who use AI to analyze coverage systematically find coverage that manual analysis misses. The technology pays for itself through increased recoveries for clients. For more on AI in insurance law practice, visit FirmAdapt's law firm solutions page.