Insurance rests on two acts: estimating a risk before covering it, then paying out quickly and fairly when it happens. AI touches both. The market for AI in insurance is estimated at 26.3 billion dollars in 2026 and could reach 114.5 billion by 2031 (Mordor Intelligence). For an insurer, a broker or a Belgian company that manages its own risks, the question is no longer whether AI is entering underwriting and claims, but where it truly creates value, and where it adds regulatory risk.
The insurance value chain is a sequence of data-intensive steps: distribute, underwrite, price, service contracts, handle claims, detect fraud. Each mixes free text, documents, photos and histories, exactly what AI handles well. That is why insurers deploy AI across the whole journey, not on a single point.
AI structures files, extracts data from documents and prepares a quote, under the control of the actuary and the underwriter.
faster quotingTriage, damage estimation from photos, drafting of letters, spotting of complex cases to escalate.
faster processingSpotting suspicious patterns and organized rings that fixed rules let through.
losses avoidedVoice and conversational agents available around the clock, answers on coverage and claim status.
always availableIt is in claims that the published results are most concrete. UK insurer Aviva rolled out more than 80 AI models in its claims domain. According to McKinsey, this cut liability assessment time for complex cases by 23 days, improved the accuracy of routing claims to the right teams by 30 percent and reduced customer complaints by 65 percent. Aviva told investors that transforming its motor claims alone saved the company more than 60 million pounds (82 million dollars) in 2024.
McKinsey finds that rewiring an entire domain, rather than stacking isolated use cases, produces most of the value: 10 to 15 percent premium growth, 20 to 40 percent lower costs to onboard a new customer and a 3 to 5 percent accuracy gain on claims. Insurers that merely line up proofs of concept stay, in the firm's words, in "pilot purgatory": the demo works, the value does not follow.
In underwriting, AI reads files, extracts the useful information and prepares a quote in hours rather than days. The gain is real, but pricing touches the customer directly: a poorly calibrated model can produce unjustified, even discriminatory price gaps. That is precisely what the European regulator is watching.
Quoting speed, decision consistency, the ability to exploit unstructured data (reports, photos, histories) that a human cannot read at scale.
Explainability of the price, no bias on sensitive variables, human oversight on refusals and edge cases, traceability for the audit and the ombudsman.
Fraud is one of the most direct arguments for AI. The Coalition Against Insurance Fraud estimates that it costs the US economy 308.6 billion dollars a year, across all lines. In Europe, Insurance Europe estimates that fraud, detected and undetected, amounts to up to 10 percent of claims expenditure. Fixed rules let new fraud and organized rings through; AI models pick up weak signals and links between files that the human eye does not see at scale.
A fraud detection model remains a decision aid, not a judge. A false positive is an honest customer wrongly accused; an opaque score is a decision that cannot be defended before an ombudsman or a court. The rule: a human decides on flagged files, the model is documented and the person concerned can obtain an explanation. Without that, the tool creates legal risk instead of reducing it.
Adoption is accelerating. In Conning's annual survey of US insurance executives, 90 percent say they are evaluating generative AI and 55 percent are already in early or full adoption, a share that has nearly doubled in a year. The most cited uses are operations and claims processing. The momentum is clear, but it says nothing about the value captured: deploying is not industrializing, and a successful pilot is still far from a transformed domain.
Insurance is one of the few sectors where the European classification is explicit. Annex III of the EU AI Act targets AI systems used for risk assessment and pricing in life and health insurance: they are high-risk. This triggers heavy obligations: a documented risk management system, data governance, human oversight, logging, transparency. Property and casualty insurance, such as motor and home, is not covered by this specific point, but the GDPR does apply to any personal data.
Obligations for the high-risk systems of Annex III were pushed back to 2 December 2027 by the "Digital Omnibus" (political agreement of 7 May 2026, confirmed by the European Parliament on 16 June 2026). This delay does not remove the obligation: it gives time to prepare the documentation and oversight. It comes on top of the GDPR, already applicable today to any health or claims data.
AI is a lever for cost and service, provided you transform a whole domain (claims or underwriting) rather than line up tools. Life and health compliance is prepared now, not at the deadline.
These gains on the insurer's side can translate into shorter payout times and better-adjusted prices. But an automated decision that concerns you (refusal, price, claim) must remain explainable and contestable.
Insurance data is among the most sensitive there is: health, wealth, claims, sometimes judicial records. Entrusting those flows to a model means knowing where they are processed and who has access. A rollout compliant with the GDPR and the EU AI Act, on infrastructure you control, remains simpler to govern than an opaque remote service, especially when the regulator can ask for accounts.
Claims or underwriting: a scope where the data exists and the gain can be measured. Avoid scattering into proofs of concept that lead nowhere.
Which personal data, which health data? Does the case fall under high-risk (life, health)? Document before deploying.
Refusal, flagged fraud, edge cases: AI prepares, the human decides. Explainability and a right of appeal built in from the design.
Time, accuracy, cost per file, satisfaction. Move from pilot to production with business metrics, not a demonstration.
Molderez Consult helps insurers, brokers and Belgian companies deploy AI on underwriting, claims and fraud: domain selection, GDPR and EU AI Act compliance, human oversight and governance, on infrastructure they control.
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