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Algorithmic discrimination is the AI-related insurance risk behind the alarm: a model can use seemingly neutral data that acts as a proxy for protected traits, potentially producing unfair prices or making coverage harder to obtain. AI use in insurance is established, but no evidence here shows that AI has broadly “destroyed” premiums—or that AI caused any particular person’s rate increase.
How AI could affect your insurance price
Insurers can use AI and other complex algorithms to gather information and assess risk. The potential concern is not only whether a model explicitly uses a protected characteristic. It may also draw on granular or nontraditional data and find patterns in variables that correlate with protected traits. That can create unfair differences in pricing or access even when the inputs appear neutral. The U.S. Government Accountability Office describes these risks, including the challenge of ensuring prohibited factors do not influence premium models (GAO report).
Data quality matters too. Information collected from sources outside an applicant’s direct control may be inaccurate, difficult to inspect, or raise privacy and ownership questions. If incorrect information affects an underwriting or pricing decision, the result could be a premium that does not reflect the applicant’s actual risk.
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EIOPA’s 2024 Digitalisation report found that 50% of non-life insurers and 24% of life insurers were using AI in some part of the value chain. Those uses included pricing and underwriting, fraud detection, and claims management. The figures measure adoption across several activities; they do not say what share of insurers use AI to set premiums (EIOPA publication).
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A higher premium is one possible result of more granular risk assessment, not an inevitable outcome of AI. Better risk estimates or automated processes could reduce costs, while new data could help some people qualify for coverage or be classified as lower risk. Conversely, people a model judges riskier could face higher prices or reduced availability. These are potential effects, not guaranteed savings or increases. EIOPA discusses the need to balance AI’s benefits and risks (EIOPA analysis); the UK Centre for Data Ethics and Innovation also describes possible effects of data-driven personal insurance (CDEI snapshot).
What to do if your rate rises or coverage is denied
- Ask for the reasons in writing. Ask which factors changed the price or decision and whether external consumer data or an automated system was involved. A rate change alone does not establish that AI played a role.
- Check the underlying information. If the insurer identifies data it used, review it for errors and ask how to correct inaccurate records. Keep copies of the decision, explanation, and any correction request.
- Compare like with like. When comparing quotes, check coverage and exclusions, premium and deductible, data requested or used, and how each insurer handles questions about adverse decisions. Different quotes do not prove that AI affected either price, and shopping around does not fix bad data or guarantee a lower rate.
- Use the complaint or appeal route that applies to your policy and location. Rules and consumer rights depend on jurisdiction and insurance line. In New York, the Department of Financial Services circular describes expectations for insurers authorized in the state that use AI systems or external consumer data in underwriting and pricing (NY DFS Circular Letter No. 7).
What New York guidance says about explanations
New York DFS issued Circular Letter No. 7 on July 11, 2024. It sets expectations for covered insurers’ governance of AI and external consumer data, including attention to actuarial validity and unfair discrimination. The insurer remains responsible for legal compliance when it uses a vendor. The letter also addresses meaningful notice and explanations for adverse underwriting or pricing actions, including information and sources used.
“An insurer may not rely on the proprietary nature of a third-party vendor’s algorithmic processes to justify the lack of specificity related to an adverse underwriting or pricing action.”
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That is New York DFS guidance in its New York context, not a nationwide rule. The NAIC says its Model Bulletin was adopted in December 2023; as of March 2026, it reported that an AI Systems Evaluation Tool was being piloted by 12 states, with adoption anticipated at the 2026 Fall National Meeting. The bulletin and working-group process are not a single nationwide statute. Check the NAIC AI topic page for subsequent developments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the rules differ by jurisdiction
United States
The GAO’s 2019 report provides background on AI, nontraditional data, and discrimination concerns; it is not a statement of current law for every insurer. New York’s 2024 circular is specific to the state and to underwriting and pricing. Applicable requirements elsewhere depend on state, policy type, and other relevant law.
European Union
The European Commission’s June 2024 explainer identifies AI systems used for risk assessment and pricing in life and health insurance as high-risk use cases under the AI Act (European Commission). EIOPA notes that sectoral insurance law continues to apply whether or not a system is classified as AI (EIOPA publication). These EU materials should not be treated as rules for other jurisdictions.
Quick Recap
What the evidence does—and does not—show
- Insurers use AI in multiple parts of their operations, but the available adoption figures do not establish how many use it specifically to set prices.
- Models can create proxy effects, and inaccurate or opaque external data can affect consumers.
- AI may contribute to higher prices or reduced access for some people, while potentially improving efficiency or expanding access for others.
- No evidence cited here attributes an individual’s premium increase to AI or establishes a universal effect on insurance prices.
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