Topic
AI in insurance
985 stories · tracked since Aug 1, 2024 · latest Sep 22, 2026
AI in insurance is the use of machine learning and generative models to price risk, process claims and answer customer questions. The term now stretches to agentic systems that complete a task end to end with only a final human check.
How AI actually gets built into an insurance workflow
An insurer, broker or claims team adopts a model built in-house or by a specialist vendor, and wires it into an existing policy administration or claims system rather than replacing it outright. Older applications score risk from structured data or read documents and photos; newer ones are generative models that draft policy language, answer customer questions, or pull details out of a submission. The most recent shift is toward agentic AI, where a system chains several of these steps together and carries out a task with only a final human check rather than a decision at every step.
Why insurers are moving now, and what makes it hard
Insurance runs on paperwork and repetitive judgment calls, which makes it a natural target for automation, and the appeal is faster underwriting, quicker claims settlement and lower handling costs. The hard part is trust: an underwriting or claims model that gets it wrong exposes the insurer to regulatory and legal liability, so regulators are asking insurers to explain how their models reach a decision and to show they are not encoding bias into pricing or claims denial. Staff also worry the technology is built to replace judgment roles rather than support them, which shapes how carefully it gets rolled out.
Who is building it, and where coverage keeps landing
The companies most active here split into three groups: large brokers and carriers such as Gallagher, Aon, Zurich and Chubb, building or buying AI into their own operations; specialist vendors such as Pace, Qumis, Federato and FurtherAI, selling AI-native underwriting and claims tools into the industry; and reinsurers and technology partners such as Willis Towers Watson and Swiss Re, supplying the risk intelligence underneath. Coverage keeps landing alongside funding rounds for these startups, new product launches and partnership announcements between an insurer and a vendor, and it increasingly overlaps with property and catastrophe coverage as AI is applied to weather and disaster risk.
Agentic AI versus the predictive models that came before it
Most of the AI already inside insurance is predictive: a model scores a risk or flags a suspicious claim, and a person decides what to do with that score. Agentic AI is different in kind rather than degree - it is given a goal, such as processing a straightforward claim or completing part of an underwriting file, and carries out the individual steps itself, stopping for a human only at exceptions or a final sign-off. That distinction is why governance has become its own topic: a scoring model is easy to audit after the fact, but a system making a sequence of decisions needs oversight built into the workflow itself.
Written by Insurtech Daily from the coverage below.
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