AI for insurance

A claim arrives on Friday afternoon and nobody opens it until Tuesday. Nothing went wrong, no rule was broken, and the customer will remember those three days longer than the settlement. Most of what AI for insurance can usefully do sits in that gap, not in the underwriting models the trade press writes about.

The three places time disappears

First notification of loss. The claim arrives by email, phone or a form, in whatever shape the customer managed. Somebody reads it, decides what it is, and starts a file. Those hours are the ones the customer judges you on, and they are almost entirely clerical.
Renewals assembled by hand. Pull the schedule, check what changed, produce the pack, chase the answer. Predictable, dull, and it slips exactly when the book is busiest.
Document requests. Certificates, schedules, proof of cover. The document exists. A person is fetching it. That is a large share of a service team's day and none of it is service.

Where regulation draws the line

This is an FCA and DGS regulated environment, and the constraints are not decoration. They decide the architecture.

No claim gets declined by a machine. Declines are decisions with consequences and appeal rights, and they carry a person's name.
No pricing or underwriting judgement without a documented, explainable basis. "The model said so" is not a basis you can defend to a regulator or to a customer.
Vulnerable customer signals stop the automation and route to a person. Getting that wrong is a regulatory problem before it is a service problem.
A complete record of what the system did, on what data, under which rule version. Skipped in demos, and the first thing anyone asks for afterwards.

From notification to open file, without a person

Claims intake that reads what arrived — email, form, photographs, a PDF of a repair estimate — and produces a structured file with the policy matched, the cover checked and the missing items listed, within minutes.
Triage by your rules: straightforward and within limits goes to fast handling; anything unusual, large or contentious is flagged to a person with the reason attached.
Renewal packs drafted from the existing policy and what changed during the year, ready for a broker to review rather than assemble.
Document and status requests answered automatically from the record, with anything that smells like a complaint routed to a person immediately.

Too early when

Policy data is spread across systems that disagree. Matching a claim to the wrong version of a policy is worse than matching it slowly.
Low claim volume and a small book. The intake gain scales with how many claims land in a week.
No written rules about what counts as straightforward. Until that exists, triage cannot be built — and writing it down turns out to be the valuable part.

The same judgement work, running now

Returns & defects handler — the closest thing to claims already running: every claim judged against the rules the moment it lands, with one batch to sign off each morning rather than a queue nobody reaches.
Sales funnel report — the watching half: roughly 750 items read every day with only the ones that have a real problem surfaced. A renewal book behaves the same way.

Questions brokers ask

Can AI settle a claim?

It can handle everything up to the decision: read what arrived, match the policy, check the cover, list what is missing and route it. The decision to pay or decline stays with a person, and the record has to show who and on what basis.

What about underwriting?

Anything touching price or acceptance needs an explainable, documented basis, which rules out the interesting-looking approaches for most brokers and MGAs. Where it does apply, the build is as much about the audit trail as about the model.

How fast does claims intake get?

Minutes rather than days for the clerical part — file opened, policy matched, missing items requested. That is the part customers judge you on, and it is available without touching the decision itself.

Will it deal with customers directly?

For status and documents, yes. Anything with distress or a complaint in it goes to a person immediately, because the regulatory and reputational cost of getting that wrong dwarfs the saving.

Is our claims data safe with a model provider?

It is when it stays in the EU, the provider does not train on it, and each automation reaches only what it needs. Those are decided before anything is built, because retrofitting means building twice.

Where these projects usually go wrong

Two places. The rules about what counts as straightforward were never written down, so triage has nothing to check against — and writing them down turns out to be most of the value. And the audit trail gets added at the end, which means building twice. If you want to avoid both, bring ten anonymised claim notifications as they actually arrived, messy ones included, and the free hour is enough to see where you stand.

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