AI in the insurance industry

An insurer lives from data: it estimates how likely a loss is, sets a premium, and pays claims. AI works at each of those steps. In underwriting, models read applications and medical or property data and suggest a risk class. In pricing, they estimate expected claims per customer group. In claims, image recognition assesses damage: the German insurance association GDV describes a customer who photographs a damaged car (in German) and gets an assessment within minutes. Fraud detection looks for suspicious patterns across claims, and chatbots and document processing take over routine mail and questions.

Generative AI added text work: summarizing claim files, drafting letters and helping agents find policy terms. Agentic AI goes a step further. The book Building AI Agents for Finance by Fayssal El Mofatiche and Hanane Dupouy builds a multi-agent pipeline for insurance claims that runs from first notice of loss through coverage check and fraud detection to a compliance sign-off with a full audit trail, the diagram shown on this page.

Germany's insurance industry has its center in Munich. Allianz and Munich Re have their headquarters there, and the InsurTech Hub Munich, backed by Allianz, Generali and Munich Re, works with startups on fraud detection, claims automation and new products, as described on the page on fintech in Munich. The data science teams of these insurers build pricing and claims models that fall under the same EU rules as banking AI.

Munich Re also turned AI risk into a product. Its aiSure cover insures the performance of AI solutions: it protects against losses caused by AI that does not work as specified, and AI vendors can use it to back performance guarantees to their clients. An AI solution must pass Munich Re's technical due diligence before it is covered. For the market, that is a sign that model errors have become a measurable, insurable risk.

The EU AI Act lists AI used for risk assessment and pricing of natural persons in life and health insurance as high-risk. An insurer that builds such a system carries the provider duties, from risk management to technical documentation; one that buys it carries the deployer duties, including human oversight and a fundamental rights impact assessment. The details of provider and deployer duties are on the page on the EU AI Act in financial services.

Most insurance AI is not high-risk: motor and property pricing, claims triage and fraud models fall outside that list. For them the European insurance authority EIOPA, seated in Frankfurt, published an Opinion on AI governance and risk management. It explains how the Solvency II Directive and the Insurance Distribution Directive apply to AI and sets expectations on data governance, record-keeping, fairness, cybersecurity, explainability and human oversight, in a risk-based and proportionate way. In Germany BaFin supervises AI that insurers use in regulated business, and it treats AI systems as ICT assets under DORA, as explained on the page on AI compliance in Germany.

Fairness is the first topic. A model trained on historical data can pick up traits such as gender or origin through proxies like occupation or postcode, even when those traits are not in the data. Actuaries and data scientists therefore test models for indirect discrimination before and after launch. Explainability is the second: when a claim is cut or refused, the customer and the ombudsman want reasons, and a model that cannot give them is hard to defend.

The third topic is fraud on both sides. Insurers use AI to find staged accidents and inflated claims, while fraudsters use generated images and documents to fake damage. Someone new to the field should learn how a claim moves through an insurer, from first notice of loss to payment, and where a model makes or supports a decision on that path. Fraud prevention in Germany covers the banking side of the same problem.

Finance Loop is the meeting place for actuaries, data scientists, claims managers and compliance staff at insurers and the fintechs around them. It connects the finance, IT and AI communities in Frankfurt and holds events in Munich, Berlin and Hamburg as well.

Finance Loop announced KI Exchange 2026 in Hamburg, a conference by Payment & Banking whose program included insurtech, fraud detection and AI governance. Finance Loop highlighted fAInance, a conference by Sopra Steria and Fraunhofer IAIS for staff of financial institutions, with stations on AI against financial crime and an AI auditor. Finance Loop network member Fayssal El Mofatiche co-wrote Building AI Agents for Finance, which includes the insurance claims pipeline shown above.

In underwriting and pricing, to estimate risk; in claims, to assess damage from photos and route files; in fraud detection, to spot suspicious patterns; and in service, with chatbots and automated document processing. Generative AI now also drafts letters and summarizes claim files.

Only in part. AI for risk assessment and pricing of natural persons in life and health insurance is high-risk. Motor, property and most claims and fraud models are not, and for those EIOPA's Opinion on AI governance sets the supervisory expectations.

aiSure is Munich Re's cover for the performance of AI solutions. It pays for losses caused when an AI solution fails to perform as specified, and AI vendors use it to back guarantees to their customers after passing Munich Re's technical due diligence.

Finance Loop runs evenings in Frankfurt and events in Munich and Hamburg where insurers, fintechs and data scientists meet, and it supports conferences on AI in finance. Dates are in the calendar on this page.