From claim inbox to decision, with a human on the line that matters

A claims team was doing everything by hand, and not because they hadn’t heard of automation. They’d avoided it on purpose: their process had to be defensible, every decision backed by evidence and a human sign-off, and no off-the-shelf tool had offered that without turning adjudication into a black box. So they read every claim — the trivial and the tricky — at the same careful pace.

The opportunity was obvious once framed correctly. Most claims were routine and well-documented. If those could be read, checked, and cleared automatically — with the same evidence trail a human would leave — the team could spend its judgement on the claims that actually needed it.

Intake that reads and cites

Incoming claims — PDFs, scans, photos — ran through source-grounded extraction. The fields each claim turned on came back as structured values, each tied to the exact place in the document it was lifted from. Nothing entered the decision without a citation, which meant the automated path produced the same kind of evidence the manual path always had.

The rule was simple: automate the reading, never the accountability.

Routing by confidence and risk

A rules engine sorted claims by confidence and risk. Well-cited, low-risk, in-policy claims cleared on the first pass. High-value claims, low-confidence extractions, and anything that tripped a defined condition were held and raised as approval cards — the claim, its cited fields, and the reason it stopped — for a human to approve, reject, or modify. Those decisions fed back into the rules, so the routing sharpened over time.

What the team got back

The routine volume stopped consuming the team’s day, and the ambiguous and high-value claims got more attention, not less. Every claim — auto-cleared or human-decided — carried a replayable trail of what was read, what was checked, and who signed off. Faster throughput and a stronger audit position, at the same time, because the evidence was built in rather than bolted on.

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The team didn’t trade accountability for speed. They automated the part that was always mechanical — reading and matching well-documented claims — and kept humans exactly where human judgement earns its keep.