Insurance is fundamentally a business of predicting risk, so it is no surprise that insurers were early adopters of data analytics. AI now touches nearly every part of the insurance value chain — from setting prices to settling claims — raising efficiency, but also questions about fairness and transparency.

Underwriting and pricing

Underwriting decides whether to insure someone and at what price. AI models analyze many more variables than traditional rating tables:

  • property characteristics from aerial and satellite imagery;
  • driving behavior from telematics in usage-based car insurance;
  • business data for commercial policies;
  • health and lifestyle information where permitted.

More precise pricing can reward lower-risk customers, but it can also make insurance unaffordable for others and blur the line between legitimate risk factors and proxies for protected characteristics.

Claims processing

Claims are where customers experience insurance most directly. AI helps by:

  • triaging claims by complexity and urgency;
  • estimating damage from photos of vehicles or property;
  • extracting information from documents, invoices and medical reports;
  • automating simple claims for fast payment;
  • drafting communications with customers.

Faster claims improve satisfaction, but automated denials have drawn criticism and legal challenges, particularly in health insurance, when decisions appear to rely on algorithms with limited human review.

Fraud detection

Insurance fraud is costly. Machine learning flags suspicious patterns — inconsistent claims, links between claimants, repairers or providers, or altered documents. Generative AI also creates new fraud risks, such as fabricated photos of damage or fake documents, which insurers must learn to detect.

Customer service and distribution

Chatbots answer policy questions, help with quotes and guide customers through claims. AI assists agents and brokers with product comparisons and documentation.

Fairness and regulation

  • In the EU, the AI Act classifies AI used for risk assessment and pricing in life and health insurance as high-risk, requiring risk management, data governance, human oversight and transparency.
  • In the US, insurance is regulated at the state level; state regulators, coordinated through the National Association of Insurance Commissioners, have issued guidance expecting insurers to govern AI systems, test for unfair discrimination and document decisions. Some states have specific rules on external data and algorithms.
  • Anti-discrimination principles prohibit using protected characteristics — and regulators increasingly scrutinize variables that act as proxies for them.

Good practices for insurers

  1. Maintain an inventory of AI models and their uses.
  2. Test models for unfair discrimination across groups.
  3. Keep humans in the loop for adverse decisions such as claim denials.
  4. Explain decisions to customers in plain language.
  5. Monitor models for drift and errors.

Examples across insurance lines

  • Motor: photo-based damage estimates, telematics pricing and detection of staged accidents.
  • Home and property: aerial imagery to assess roofs and wildfire or flood exposure, and fast assessment after natural disasters.
  • Health: claims coding checks, prior authorization processing and fraud detection — the area where automated decisions have drawn the most criticism.
  • Life: accelerated underwriting that uses data to reduce the need for medical exams, regulated as high-risk in the EU.
  • Commercial: summarizing complex submissions and analyzing risks such as cyber exposure.
  • Reinsurance and catastrophe modelling: machine learning to improve models of extreme weather and other catastrophic events.

Each line has different data, regulation and customer expectations, so insurers rarely use one AI approach across the whole business.

What to watch next

  • State-level AI rules for insurers in the US.
  • Implementation of the EU AI Act for life and health insurance pricing.
  • Litigation over automated claim denials.
  • Detection of AI-generated fraudulent claims evidence.

Key takeaways

  • AI is used across insurance: underwriting, pricing, claims, fraud detection and service.
  • Imagery, telematics and documents feed more granular risk models.
  • Automated claim denials have drawn criticism and lawsuits.
  • The EU AI Act treats life and health insurance pricing AI as high-risk.
  • Insurers are expected to govern AI, test for discrimination and keep humans in adverse decisions.

Frequently asked questions

Can my insurer use my driving data?

Usage-based policies collect driving data with your consent. Check what is collected, how it affects your price and how long it is kept.

Can I challenge an AI-based claim decision?

Yes. You can ask for an explanation, request human review and use the insurer’s complaint process or your regulator or ombudsman.

Does AI make insurance cheaper?

It can lower costs and prices for some customers, but more granular pricing can raise prices for others considered higher risk.