AI in claims processing

The short answer

Most claims cycle time is not spent deciding. It is spent waiting: for a missing document, for a file to be assembled, for a claim to reach the right queue, for someone to read forty pages of attachments.

That waiting is where AI belongs. The adjudication itself, especially on anything contested or high value, stays with a person, because that is where liability and judgement live.

Break the process into steps first

Intake, completeness check, triage, evidence assembly, adjudication, communication, payment. These have completely different automation profiles and lumping them into one AI claims project is how the work becomes unscopeable.

Measure where the days actually go before choosing. Teams consistently target adjudication, which is the smallest slice of elapsed time and the hardest to automate safely, while intake and assembly sit untouched.

Intake and completeness

A claim submitted incomplete is a claim that will wait for a human to notice and a claimant to respond. Checking completeness at the moment of submission, and saying precisely what is missing, removes a whole round trip from the cycle.

This is high value and low risk: the system is not deciding anything, it is comparing what arrived against what the policy requires and telling someone immediately.

Triage and routing

Classifying a claim by type, severity, complexity and the specialism it needs is a genuine AI task and it compounds, because everything downstream is faster when the claim starts in the right place.

Keep the classification explainable and keep a confidence threshold that routes uncertain cases to a human rather than guessing. A misrouted complex claim costs more than the routing saved.

Evidence assembly is the biggest win

Reading the police report, the photographs, the invoices, the policy document and the prior correspondence, then producing a structured summary with every figure linked to its source, is hours of work per claim.

Done properly, the adjudicator opens a file that is already organised, with citations they can click. The value is not the summary text, it is the traceability, which is a RAG problem more than a generation one.

Why adjudication stays human

It is contractual interpretation with money and legal exposure attached, applied to facts that are often disputed. A probabilistic system is the wrong instrument for that, regardless of how good the summary was.

There is a narrow exception: simple, low value, high volume claims with unambiguous criteria can be straight through processed with tight rules and sampling. That is a rules engine with AI on the intake, not an AI making judgement calls.

Guardrails and audit

Every extracted figure carries a link to the page it came from. Nothing is asserted that is not in the file. Coverage language is quoted, never paraphrased into something that could be read as a coverage position.

And log everything, because a claim can be reopened, disputed or litigated years later and you will need to reconstruct exactly what the system showed the adjudicator. That is the operational point of observability, and it is the specific requirement behind AI for insurance.

How you measure it

Cycle time from submission to decision, first pass completeness rate, rework per claim and adjudicator time per file. Then leakage and reversal rate, to confirm speed did not cost accuracy.

Track the human override rate too. If adjudicators consistently disagree with the triage, the classification is wrong and the summary is being ignored, which means you built something nobody uses. That is the pattern in measuring AI ROI.

How to sequence it

One claim type, one line of business, intake and completeness first. That produces a measurable cycle time change in weeks and it teaches you what the documents actually look like, which no amount of planning will.

Assembly second, triage third, adjudication support last if at all. Building in that order is what makes it production AI, end to end rather than a demo on clean sample claims.