Agentic AI for claims processing takes a claim from first notice of loss (FNOL) to a settlement recommendation — extracting the claim, verifying coverage, setting a reserve, and then either settling simple losses straight through, routing complex or disputed claims to an adjuster, or referring suspected fraud to the special investigations unit (SIU). Claims is the most heavily automated function in insurance and the leading area for agentic deployment, as covered in agentic AI in insurance.
The claims workflow, automated
A claim is a multi-step process that has historically passed through several hands. An agentic system compresses it:
- Intake (FNOL). Capture the loss across channels — web, app, voice — and extract structured data from unstructured descriptions and documents.
- Coverage verification. Check the loss against the policy: is it covered, what are the limits, deductibles, and exclusions?
- Reserving. Set an initial reserve based on the loss characteristics.
- Disposition. Settle simple, low-value losses straight through; route complex, disputed, or large losses to an adjuster; refer claims with fraud indicators to the SIU.
The agent assembles and recommends; a human owns the consequential or contested calls. McKinsey frames 2026 as the shift to the agentic era, with trust as the gating factor — and nowhere is policyholder trust more visible than in how a claim is handled.
Straight-through, carefully
For low-complexity, high-frequency losses — minor auto, simple property — end-to-end automation to payment is technically achievable. The open question is rarely capability; it is governance and policyholder fairness. Settling automatically means the system has verified coverage correctly, priced the loss fairly, communicated clearly, and can show its work if challenged. The right starting point is FNOL intake and triage, where volume is high and a human is already reviewing, then widening straight-through settlement as evaluation builds confidence — not automating everything at once. Gartner predicts more than 40% of agentic AI projects will be canceled by the end of 2027, and over-reaching on autonomy is a reliable way to join them.
What insurance regulation requires
Claims handling is governed whether or not AI is involved. In the US, the NAIC AI Model Bulletin — adopted in December 2023 and taken up by a growing number of states — sets expectations on AI governance, accountability, and testing for unfair discrimination, layered on top of existing unfair-claims-settlement-practices law. In the EU, the EU AI Act attaches its high-risk obligations principally to risk assessment and pricing in life and health insurance, but the transparency and oversight expectations inform claims practice too.
Meeting that bar comes down to the same controls that govern every regulated agent: a complete audit trail of coverage, reserve, and settlement decisions, and a human-in-the-loop checkpoint for disputed and complex claims. The same discipline runs through its sibling workflow, agentic AI for insurance underwriting.
Talk to BlackGrid about automating claims without sacrificing fairness or auditability.