Claims, underwriting and fraud are where insurance AI pays for itself — and where a wrong, unexplained decision carries regulatory and reputational cost. From August 2026 the EU AI Act treats much life- and health-insurance risk assessment and pricing as high-risk. We build AI insurers can put in front of a regulator: explainable decisions, audit trails, and human oversight where it matters.
Every claim, policy and submission is a stack of unstructured documents feeding a decision that has to be fair, defensible and fast. Insurers see the upside — faster claims, higher straight-through processing, sharper fraud detection — but the constraints are real. The challenge in 2026 is shipping AI that holds up under a regulator’s scrutiny, not just a demo.
First notice of loss arrives by phone, app, email and PDF — police reports, invoices, medical records, photos. Adjusters re-key it by hand before any decision can start. The data exists; it just isn’t structured, validated or routed.
A model that prices risk well but can’t justify a declination is a liability. Regulators — from the EU AI Act to the NAIC model bulletin and state laws — expect reason codes, bias testing and a written explanation when AI drives an adverse decision.
From August 2026, AI used for risk assessment and pricing in life and health insurance is treated as high-risk under the Act — bringing obligations for risk management, data governance, technical documentation, human oversight and logging. Compliance is now a design constraint, not a sign-off at the end.
Models produce a result, then someone re-keys it into a policy admin system from the 1990s and an adjuster reviews it anyway “to be safe.” The pilot works; the savings don’t arrive because the new workflow runs in parallel with the old one instead of replacing it.
A claim arrives as a messy bundle — photos, forms, medical reports, and a story that rarely comes in order. We build the systems that read that bundle, structure it, check it against the policy, and route the clean cases straight through — while every decision stays explainable and every contested case lands with an adjuster who has the full picture.
Talk through your claims and underwriting workflow →The direction of travel across the sector is consistent: when intake is structured and the simple cases flow through automatically, cycle times fall and adjusters spend their time where judgement is actually needed. We scope to the lines and claim types where the economics hold — not a blanket promise to automate everything.
Lower claims cycle time on clean, low-complexity claims
Straight-through processing on well-shaped personal-lines claims
More fraud caught than rules alone, routed to investigators not auto-denied
Adjuster time redirected from data entry to judgement calls
Every automated decision logged, explainable and reviewable
From first notice of loss to renewal, these are the workflows where insurers see the clearest return — each one drawing on a specific area of our expertise.
Extract structured data from the documents that drive a claim — police reports, invoices, medical records, loss-adjuster notes — so intake is structured and validated in seconds instead of re-keyed by hand.
Document Intelligence →Risk-assessment and pricing support that puts a recommendation and its reasons in front of the underwriter — with reason codes and audit trails so an adverse decision can always be explained.
Decision Intelligence →Models that surface suspicious claims and forecast loss exposure — flagging anomalies rule-based filters miss, and routing them to investigators rather than auto-denying a policyholder.
Predictive Analytics →Agents that handle the repetitive work around a claim or policy change — checking coverage, requesting missing documents, updating records — inside clear guardrails, with a human owning every decision that affects a payout.
AI Agents →Answers grounded in policy wordings, endorsements, claims manuals and prior decisions — with citations to the clause that drove the answer, so adjusters and agents act on the document, not a guess.
Knowledge Retrieval →The governance layer that makes the rest deployable under the EU AI Act and insurance regulators — bias testing, explainability, decision logging and human oversight built in, not bolted on at sign-off.
AI Governance →A model that is accurate but unaccountable will not survive contact with a regulator or a complaint. These are the constraints we design around from day one — not the ones we discover at audit.
Where AI assesses risk or sets prices for life and health cover, the Act’s high-risk obligations apply from August 2026 — risk management, data governance, technical documentation, logging and human oversight. We treat these as design requirements from the first sprint.
Reason codes, adverse-action explanations and bias testing across protected characteristics — aligned to the NAIC model bulletin and state requirements. If a decision can’t be explained to the policyholder it affects, it isn’t ready.
Every automated decision logged, reproducible and reviewable, with a clear point where a person can intervene. Humans keep authority over declinations, payouts and disputed claims — the AI does the preparation, not the final call.
Medical and financial data handled inside your access controls and residency requirements, integrated with the policy admin and claims systems you actually run — so AI replaces a step in the workflow rather than running beside it.
Not every claim should be automated. We identify the lines and claim types where straight-through processing is safe and routes the rest to people — so the savings are real and the edge cases stay supervised.
The models, the prompts, the integrations, the documentation and the evaluation harness — all yours, with your team enabled to operate and extend them. Enablement is part of the engagement, not an upsell.
No. It means AI used to assess risk and set prices for life and health cover is treated as high-risk from August 2026, which brings obligations — risk management, data governance, documentation, human oversight and logging. You can absolutely use AI; you have to be able to show how it decides. We build to those obligations from the start rather than retrofitting them.
For a defined slice, yes — simple personal-lines claims that pass validation and fraud screening can settle straight through. The point is not to remove adjusters; it is to free them from the routine cases so they spend their time on the complex, contested and high-value claims where judgement earns its money. We scope which claim types are safe to automate before we automate anything.
Bias testing across protected characteristics, reason codes on every decision, and adverse-action explanations — monitored continuously, not signed off once. This aligns with the NAIC model bulletin and state requirements. If a model prices well but can’t justify a declination, we treat that as a defect, not an acceptable trade-off.
No, but it shapes the design. The common failure is AI bolted on top, with outputs re-keyed by hand into a legacy system — so the savings never arrive. We integrate with the systems you run and design the AI to replace a step in the workflow, not run in parallel with it.
Inside your access controls, residency requirements and retention policies — with PII handling and logging built into the system. Sensitive data stays governed the same way it is everywhere else in your business; the AI doesn’t become a new place for it to leak.
Yes. The models, prompts, integrations, documentation and evaluation harness are all yours, and your team is enabled to operate and extend them. We design every engagement so you are not dependent on us to keep it running.
A 30-minute conversation with a senior consultant. Bring a claims, underwriting or fraud workflow that’s stuck in pilot, or a compliance bar you’re not sure how to clear. We’ll tell you where AI pays off, where it doesn’t, and what it takes to ship something a regulator would accept.
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