Board Case Study · Insurance (Claims)
The AI claims engine denied thousands of claims in seconds, too fast, a regulator says, to have reviewed them. The loss ratio improved. Now the bad-faith suits are consolidating.
The situation
A public insurer deployed an AI system to adjudicate claims at scale. It improved the loss ratio and was praised for efficiency. A regulator and a class of policyholders now allege the model denied or closed claims in seconds, too quickly to constitute the meaningful review that good-faith claims handling and state law require.
Some denials lacked individualized assessment, and the model may have been tuned toward outcomes that favored the insurer. Bad-faith litigation, market-conduct exam exposure, and reputational damage are mounting. The efficiency the board celebrated is now the core of the allegation.
The board must address remediation for wrongly denied claimants, the appropriate role of human review in claims, and its own oversight of an AI system making decisions that directly affect people in moments of loss.
“The system closed claims faster than anyone could read them, and our loss ratio loved it. Was that efficiency, or was it bad faith?”
Chair, Audit Committee
The decision on the table
The full case, scenario architecture, board materials, and facilitation notes, is shared with boards and partners on request.