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AI is changing insurance by bringing automated analysis and decision support into workflows such as pricing, underwriting, claims, fraud detection, policy issuance, and health-plan administration. But reported interest is not the same as deployment: the National Association of Insurance Commissioners (NAIC) figures below combine insurers that said they already use AI or machine learning (AI/ML) with those planning to use or explore it.
What the NAIC adoption figures do—and do not—show
The NAIC’s summaries of insurer surveys show reported use, plans to use, or plans to explore AI/ML across four insurance lines. Each percentage applies to respondents in a particular survey, not to every insurer in that line or to the share with AI already running in production.
| Insurance line | Respondents reporting use, plans to use, or plans to explore AI/ML | Survey summary |
|---|---|---|
| Auto | 88% of 193 responding insurers | NAIC, 2022; aggregate report issued December 2022 |
| Home | 70% of 194 responding insurers | NAIC, 2023; aggregate report issued August 2023 |
| Life | 58% of 161 responding insurers | NAIC, 2023; aggregate report issued December 2023 |
| Health | 92% of 93 responding insurers | NAIC, 2025; aggregate report issued May 2025 |
The combined response category matters: a respondent that was exploring AI counts alongside one already using it. The surveys also come from different years and respondent groups, so their percentages should not be treated as a direct ranking of adoption or as evidence of business results. The NAIC’s summaries describe U.S. insurer activity; they do not establish global adoption, insurer-by-insurer performance, or the effects of AI on consumers.
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AI does not perform one uniform role across insurance. The use depends on the line of business and the task: a model might analyze information for a person, recommend an outcome, or automate part of a process. The NAIC’s examples cover multiple stages of the policy and claims lifecycle.
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| Insurance line | Reported or described applications |
|---|---|
| Property and casualty, including auto and home | Targeted marketing; renewal evaluation and property inspection; machine-learning risk scoring and rate-factor relativities; analysis of accident images; estimates of ultimate claim settlement values; and fraud detection. |
| Life | Targeted offers; support for assigning underwriting risk classes; and shortening policy issuance, including support for approval or denial decisions. |
| Health | Prior authorization; fraud detection; pricing and plan design; processing; risk adjustment; sales and marketing; and claims adjudication. |
These examples describe potential workflow applications, not a guarantee that any insurer uses every application or that a model makes the final decision. A pricing model, for example, can inform risk scoring or rate factors; an image-analysis system can help assess accident damage; and a health system can support prior authorization or claims adjudication. Those tasks differ in the data involved, the people affected, and the consequences of an incorrect result.
Marketing and model sourcing
The NAIC summary says roughly half of the models insurers used for marketing were developed by third-party vendors. It also says auto and home insurers mostly developed pricing and underwriting models in-house. Those observations apply to the model categories and lines described in the summary; they should not be generalized to all insurance models or all insurers.
Automation, assistance, and the importance of review
A useful question is not simply whether an insurer uses AI, but what authority the system has in a particular workflow. The NAIC describes systems that may automate, augment, or support human decisions. An automated step may produce an outcome directly; an augmenting system may combine a recommendation with a person’s judgment; a support tool may organize or analyze information without deciding the case.
The practical stakes depend on the decision. A marketing selection and a claim decision can affect consumers in different ways; so can an underwriting classification, a renewal evaluation, or a prior-authorization determination. When assessing a system, insurers and regulators need to understand what outcome it influences, whether a person reviews the result, and what happens when the information or recommendation is wrong. The NAIC sources describe governance and examination expectations, but do not establish a single human-review procedure that applies to every use.
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Potential efficiencies come with consumer and operational risks
The NAIC’s adopted Model Bulletin recognizes possible benefits such as innovation, improved consumer interfaces, process simplification, efficiency, and accuracy. These are potential benefits, not proof that a particular deployment has delivered them. The NAIC materials do not establish quantified savings, faster claim cycles, improved accuracy, or better consumer outcomes attributable to AI.
The bulletin also identifies risks that matter when systems influence consumer-impacting decisions:
- Inaccuracy: flawed data, assumptions, or model outputs can lead to an incorrect assessment or action.
- Unfair discrimination: an AI-supported decision may produce unfairly discriminatory outcomes, which remain subject to applicable insurance laws.
- Data vulnerability: the information used or handled by a system may be exposed to security or privacy risks.
- Limited transparency or explainability: an insurer may have difficulty understanding or explaining how a system reached or supported an outcome.
These risks make model oversight an operational issue as well as a compliance issue. The NAIC’s 2020 AI principles emphasize fairness and ethical use, accountability, compliance, transparency, and safety, security, fairness, and robustness. In practice, that calls for understanding the data and model involved, assessing how the system affects consumers, and checking whether its performance and outcomes remain appropriate over time.
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The NAIC adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023. It reminds insurers that decisions or actions affecting consumers and made or supported by advanced analytical and computational technologies must comply with applicable insurance laws, including laws addressing unfair trade practices and unfair discrimination. It also describes expectations for insurer governance and information regulators may request during an investigation or examination.
The bulletin is model guidance, not a single nationwide statute. Its legal effect depends on state action, and insurers must consider the laws applicable in the jurisdictions where they operate. The bulletin’s adoption does not replace other applicable legal requirements or determine the legal position in every state.
Regulatory evaluation work
The NAIC topic page, last updated April 3, 2026, said that a 12-state pilot of an AI Systems Evaluation Tool was underway as of March 2026 and that adoption was anticipated at the 2026 Fall National Meeting. That page records an anticipated event; it does not confirm whether adoption later occurred. The NAIC’s 2026 working-group charge includes researching insurer AI, monitoring regulatory developments, and facilitating regulatory evaluation of AI systems.
Questions that help assess an insurer’s AI use
Whether evaluating a policy decision, a claims workflow, or an insurer’s governance, these questions help make an AI use case concrete:
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- What decision or process does the system affect? Identify the specific stage—such as pricing, renewal, underwriting, claims, or prior authorization—and whether the tool acts, recommends, or supplies information.
- What data does it use, and where does that data come from? Consider data quality and provenance, and whether a third-party vendor developed or supplies the model.
- What are the consequences of an incorrect result? The impact and reversibility of an error vary by workflow and consumer.
- How is the system evaluated and monitored? Ask how the insurer checks accuracy, fairness, security, and continuing performance, and how it responds when results raise concerns.
- Can the insurer explain the system’s role? Governance should make clear how outputs contribute to consumer-impacting decisions and what information can be provided to regulators during an examination or investigation.
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