Understanding the challenge
The insurer managed claims one file at a time, and it cost them. Overpayments slipped through, fraud and red flags surfaced late, reserves were set on gut feel and revised after the fact, and injured workers who could have returned to work sooner waited because no one caught the case in time. "We were always reacting to a claim after it had already gone sideways," the insurer's VP of claims said. The signals that could have warned an adjuster early were spread across the claims system, medical bills, and spreadsheets, and past AI tools never fit how adjusters actually worked, so nothing stuck.
Our approach
The insurer did not need another report. It needed AI that watches every claim and flags the ones that need attention, within limits its adjusters set. We combined three of our services:
• AI Operations Design at the core: an operating layer that monitors medical, payment, and claim signals, flags likely fraud, leakage, and reserve gaps early, and drafts the next step for an adjuster to approve.
• Workflow automation for claims intake, capturing each claim, routing it to the right adjuster, and feeding the operating layer with clean, current data.
• A Salesforce integration so adjusters work from one live claim record and every action is logged, not scattered.
• A governed model with clear boundaries and human sign-off, built for a regulated insurer, so every flag is explainable and an adjuster owns each decision.
Technical innovation
At the core is an AI layer that operates rather than just answers: it watches each claim's medical, payment, and behavioral signals, detects the patterns that precede overpayment, fraud, or a mis-set reserve, and acts within defined boundaries, flagging the claim, drafting the intervention, or recommending a reserve change for an adjuster to approve. Self-calibrating models learn from every closed claim, so the flags get sharper each month, and because everything is logged and explainable, the layer fits the oversight and reserving discipline a regulated insurer must maintain.
Outcome
Avoidable claims costs fell by 27% as the layer caught overpayments, fraud, and reserve gaps before they compounded, and injured workers got earlier intervention because the right cases surfaced in time. Adjusters worked from one current claim picture in Salesforce instead of chasing scattered data, intake flowed straight to the right person, and every flag stayed explainable and adjuster-approved. "We get ahead of the claims that matter now, and we can defend every decision the system helped us make," the VP of claims said.
