AI Insurance Regulation in 2026: What Insurers, Policyholders and AI Vendors Need to Know
Quick Answer: The United States does not currently have one comprehensive federal AI insurance law governing every insurer and every use of artificial intelligence. Instead, AI used by insurers is regulated through existing state insurance laws and regulations, NAIC model guidance, market-conduct and financial examinations, data and privacy requirements, unfair-discrimination rules, and increasingly specific regulatory frameworks for AI, predictive models and third-party data.
Artificial intelligence is changing insurance.
Algorithms can help determine risk.
Machine-learning models can influence premiums.
Computer vision can analyse property and vehicle damage.
Predictive analytics can identify potentially fraudulent claims.
Generative AI can assist customer-service and administrative functions.
Third-party vendors can supply data and predictive models that influence underwriting and pricing.
But one legal question sits behind all of these applications:
Who regulates AI when it is used by an insurance company?
The answer is more complicated than simply saying “AI law”.
In the United States, insurance regulation is predominantly state-based.
That means insurers must consider the laws and regulations applicable in the states in which they operate, while also accounting for national regulatory coordination through the National Association of Insurance Commissioners (NAIC).
The NAIC adopted its Principles on Artificial Intelligence in 2020.
It subsequently adopted the Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023.
The Model Bulletin establishes expectations concerning responsible AI governance and reminds insurers that decisions made or supported by AI must comply with applicable insurance laws and regulations. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
By 2026, the regulatory conversation has moved beyond broad principles.
The NAIC is developing practical regulatory tools.
Its Big Data and Artificial Intelligence Working Group has been developing an AI Systems Evaluation Tool intended to help regulators assess how insurers use AI, their governance and risk-management practices, potentially high-risk models and the data used as model inputs. As of March 2026, the tool was being piloted by 12 states, with anticipated adoption at the 2026 Fall National Meeting. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
At the same time, the NAIC's Third-Party Data and Models Working Group is developing a framework for regulatory oversight of third-party data and predictive models. Its 2026 exposure specifically addresses third-party vendors and property-and-casualty pricing and underwriting data and models. ([content.naic.org](https://content.naic.org/committees/h/third-party-data-models-wg?utm_source=chatgpt.com))
The result is an important shift:
Insurance AI regulation is moving from principles toward examination, testing and accountability.
This article explains what that means in 2026.
Legal disclaimer: This article provides general educational information and is not legal, insurance, actuarial, financial or regulatory advice. Insurance regulation varies by state, insurance product and factual circumstances.
Key Takeaways
- The United States does not currently have one comprehensive federal AI insurance law.
- Insurance AI is primarily governed through state insurance regulation.
- The NAIC plays a major coordinating and model-guidance role.
- The NAIC adopted its AI Principles in 2020.
- The NAIC adopted its AI Model Bulletin in December 2023.
- AI-supported insurance decisions remain subject to applicable insurance laws and regulations.
- Insurers need governance and risk-management processes for material AI systems.
- AI regulation covers underwriting, pricing, claims, fraud detection and other insurance functions.
- Third-party data and predictive models are becoming a major regulatory focus.
- The NAIC is developing practical tools for regulators to evaluate insurer AI systems.
- Market-conduct examinations are being adapted to address algorithms and AI.
- Accuracy alone is not enough; fairness, data quality, governance and consumer impact matter.
- AI vendors should expect greater scrutiny because their technology can influence regulated insurance decisions.
What Is AI Insurance Regulation?
Quick Answer: AI insurance regulation refers to the collection of laws, regulations, regulatory guidance and supervisory practices governing insurers' use of artificial intelligence and related technologies.
It can involve:
- Insurance law.
- Unfair trade-practice rules.
- Consumer protection.
- Privacy and data requirements.
- Rate regulation.
- Market-conduct standards.
- Model governance.
- Third-party vendor oversight.
Is There a Federal AI Insurance Law in the United States?
Quick Answer: There is not currently one comprehensive federal statute that governs every use of AI by every U.S. insurer.
Insurance regulation remains substantially state-based.
Federal laws can nevertheless affect insurance AI indirectly or directly depending on the particular activity, data and consumer relationship.
Insurers therefore need to evaluate multiple legal regimes rather than search for a single “AI Insurance Act”.
Why Is Insurance AI Regulation Mostly State-Based?
Quick Answer: The U.S. insurance regulatory system has historically been based primarily on state regulation.
State insurance departments regulate insurers operating within their jurisdictions.
The NAIC helps coordinate these state regulators by developing:
- Model laws.
- Model regulations.
- Guidance.
- Examination standards.
- Regulatory tools.
This structure is especially important for AI because the same insurer may deploy a model across multiple states with different legal requirements.
What Is the NAIC?
Quick Answer: The National Association of Insurance Commissioners is the U.S. standard-setting and regulatory-support organisation through which state insurance regulators coordinate on insurance issues.
The NAIC is not itself a federal insurance regulator.
Its model laws, bulletins and frameworks generally require state adoption or implementation before they become binding state law.
What Are the NAIC AI Principles?
Quick Answer: The NAIC AI Principles provide a foundational framework for responsible use of artificial intelligence in insurance.
The principles were adopted by NAIC membership on August 14, 2020. ([content.naic.org](https://content.naic.org/committees/h/big-data-artificial-intelligence-wg?utm_source=chatgpt.com))
They address broad themes including:
- Fairness.
- Accountability.
- Transparency.
- Security.
- Governance.
- Regulatory compliance.
They provide the conceptual foundation for subsequent AI insurance guidance.
What Is the NAIC AI Model Bulletin?
Quick Answer: The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers provides guidance and regulatory expectations concerning insurers' use of AI.
It was adopted in December 2023.
The NAIC explains that the bulletin establishes guidelines and expectations for responsible AI use and reminds insurers that AI-supported decisions must comply with applicable insurance laws and regulations. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
Is the NAIC AI Model Bulletin a Law?
Quick Answer: No. The Model Bulletin is regulatory guidance rather than a federal statute.
Its legal effect depends on state adoption, implementation and the authority under which a state insurance regulator applies it.
However, insurers should not dismiss it simply because it is not itself a statute.
State regulators can use guidance to inform examinations, supervisory expectations and regulatory discussions.
What Does the NAIC AI Model Bulletin Require From Insurers?
Quick Answer: The bulletin expects insurers to establish governance and risk-management processes appropriate to their use of AI.
Relevant areas include:
- Governance.
- Risk management.
- Data management.
- Model validation.
- Testing.
- Documentation.
- Monitoring.
- Third-party oversight.
The precise implementation depends on the state regulatory framework.
Does AI Have to Comply With Existing Insurance Law?
Quick Answer: Yes.
This is one of the most important principles in insurance AI regulation.
The NAIC explains that decisions or actions made or supported by AI must comply with applicable insurance laws and regulations. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
Therefore:
New technology does not create a regulatory exemption.
Which Insurance Functions Can AI Regulate or Affect?
Quick Answer: AI can affect almost every major stage of the insurance lifecycle.
| Function | Potential AI Use |
|---|---|
| Marketing | Customer targeting |
| Underwriting | Risk classification |
| Pricing | Premium modelling |
| Claims | Automated processing |
| Fraud | Suspicious-claim detection |
| Customer service | Generative AI assistants |
| Operations | Workflow automation |
How Is AI Underwriting Regulated?
Quick Answer: AI underwriting remains subject to the insurance laws governing underwriting practices, including applicable requirements concerning unfair discrimination, data and consumer treatment.
Insurers should consider:
- Data sources.
- Predictive models.
- Risk classification.
- Fairness.
- Model validation.
- Consumer impact.
Accelerated underwriting is a particularly important area because it can combine external data with predictive models and machine learning.
What Is Accelerated Underwriting Regulation?
Quick Answer: Accelerated underwriting regulation concerns the use of technology and predictive models to streamline underwriting, particularly in life insurance.
The NAIC has developed specific regulatory guidance for regulators reviewing accelerated-underwriting programmes.
Relevant concerns include:
- External data.
- Predictive models.
- Data quality.
- Potential unfair discrimination.
- Governance.
How Is AI Insurance Pricing Regulated?
Quick Answer: AI pricing remains subject to applicable state rate-regulation and unfair-discrimination requirements.
An insurer cannot assume that a predictive model is lawful merely because it improves loss prediction.
Regulators may consider:
- Data.
- Variables.
- Actuarial justification.
- Rate methodology.
- Consumer outcomes.
Can AI Pricing Be Discriminatory?
Quick Answer: Potentially.
AI can create discriminatory outcomes through:
- Historical data.
- Proxy variables.
- External data.
- Model design.
The NAIC's AI framework expressly identifies unfair discrimination as an important concern in the use of AI by insurers. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
How Is AI Claims Processing Regulated?
Quick Answer: AI claims systems remain subject to applicable claims-handling requirements.
AI may assist with:
- Claims intake.
- Image analysis.
- Claims triage.
- Fraud detection.
- Damage estimation.
But an insurer remains responsible for compliant claims handling.
Can an AI System Deny an Insurance Claim?
Quick Answer: AI may contribute to a claim decision, but the legality of an automated denial depends on the applicable state law, policy terms and claims-handling requirements.
The critical distinction is:
AI recommendation ≠ automatic legal entitlement to deny.
Complex or disputed claims may require appropriate human review.
How Is AI Insurance Fraud Detection Regulated?
Quick Answer: AI fraud detection is subject to the same broader insurance framework governing fraud investigation and claims handling.
AI can identify:
- Anomalies.
- Suspicious patterns.
- Relationships between claims.
- Potential fraud indicators.
But:
Fraud score ≠ proof of fraud.
False positives can create consumer harm.
What Is AI Insurance Discrimination?
Quick Answer: AI insurance discrimination occurs when AI-supported insurance practices produce unlawful or unfair discriminatory treatment.
Potential sources include:
- Protected characteristics.
- Proxy variables.
- Historical data.
- External data.
- Biased model design.
What Is Proxy Discrimination in Insurance AI?
Quick Answer: Proxy discrimination occurs when apparently neutral variables indirectly correlate with protected characteristics and contribute to discriminatory outcomes.
Examples can include certain geographic or behavioural variables.
The legal significance depends on the applicable law and insurance product.
What Is the AI Systems Evaluation Tool?
Quick Answer: The AI Systems Evaluation Tool is an NAIC-developed regulatory tool intended to help insurance regulators evaluate insurers' use and governance of AI.
The tool is designed for use in contexts including:
- Market-conduct examinations.
- Financial analysis.
- Financial examinations.
It seeks information concerning:
- The extent of AI use.
- AI governance.
- Risk mitigation.
- Potentially high-risk models.
- Data used as model inputs.
As of March 2026, the tool was being piloted by 12 participating states. The NAIC anticipated adoption at its 2026 Fall National Meeting. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
Why Is the AI Systems Evaluation Tool Important?
Quick Answer: It represents a transition from general AI principles toward structured regulatory examination.
NAIC President Scott White described the tool as providing insurance departments with a structured and consistent way to understand how insurers use AI and how effective their governance and oversight systems are. ([content.naic.org](https://content.naic.org/article/evolving-marketplace-continued-state-leadership-naic-president-white-2026-spring-national-meeting?utm_source=chatgpt.com))
This is a major development.
The regulator is no longer asking only:
“Do you have an AI policy?”
It can increasingly ask:
“Show us how your AI systems actually operate.”
What Will Regulators Look for During an AI Examination?
Quick Answer: Regulators are increasingly interested in governance, risk management, model characteristics, data inputs and consumer impact.
Potential examination areas include:
- AI inventory.
- Governance structures.
- Model-risk management.
- Data governance.
- Validation.
- Testing.
- Monitoring.
- Third-party vendors.
- Consumer complaints.
What Is AI Model Governance?
Quick Answer: AI model governance is the framework through which an insurer controls the development, approval, deployment, monitoring and retirement of AI systems.
A governance framework can include:
- Model inventories.
- Risk classification.
- Approval procedures.
- Validation.
- Change management.
- Performance monitoring.
- Incident management.
Should Every AI Model Be Treated the Same?
Quick Answer: No.
A chatbot answering general customer questions presents different risks from a model determining insurance eligibility or pricing.
A risk-based approach can classify systems as:
| AI System | Potential Risk |
|---|---|
| Administrative chatbot | Lower |
| Claims document extraction | Moderate |
| Fraud scoring | High |
| Underwriting model | High |
| Pricing model | High |
| Automated claim denial | Very high |
The appropriate controls should reflect the potential consumer and regulatory impact.
What Is Third-Party AI Regulation in Insurance?
Quick Answer: Third-party AI regulation concerns insurers' use of external vendors providing data, predictive models, algorithms or other technology affecting regulated insurance decisions.
This is one of the fastest-developing areas of U.S. insurance AI regulation.
The NAIC created the Third-Party Data and Models Working Group in 2025 after the former task force was renamed. Its 2026 charge is to develop a framework for regulatory oversight of third-party data and predictive models. ([content.naic.org](https://content.naic.org/committees/h/third-party-data-models-wg?utm_source=chatgpt.com))
What Is the NAIC Third-Party Data and Model Framework?
Quick Answer: It is an emerging regulatory framework intended to address oversight of third-party data and model vendors and their products and services.
In July 2026, the NAIC exposed a framework specifically addressing:
- Third-party data and model vendors.
- Property-and-casualty pricing data.
- Property-and-casualty underwriting data.
- Predictive models.
The public-comment period ended August 5, 2026. ([content.naic.org](https://content.naic.org/committees/h/third-party-data-models-wg?utm_source=chatgpt.com))
As of August 2026, this should therefore be described as an exposed regulatory framework, not as a universally binding final regulation.
Why Are Third-Party AI Vendors Important?
Quick Answer: An insurer may rely on a vendor's model without having developed the model internally.
For example:
Vendor → Data → Predictive Model → Insurer → Premium
Or:
Vendor → Fraud Score → Insurer → Claim Investigation
The regulator may therefore need to understand the vendor's role in the insurance decision.
Can Insurers Blame AI Vendors for Regulatory Problems?
Quick Answer: Outsourcing AI does not automatically eliminate the insurer's regulatory responsibilities.
The NAIC's current AI materials emphasise that existing insurance laws apply to AI-supported decisions, including where third-party systems are involved. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
Insurers should therefore conduct appropriate vendor due diligence.
What Should an AI Vendor Contract With an Insurer Include?
Quick Answer: Contracts should address data, model performance, validation, auditability, security, changes and regulatory cooperation.
Important provisions can include:
- Data provenance.
- Model documentation.
- Validation standards.
- Audit rights.
- Regulatory access.
- Material-change notification.
- Incident notification.
- Security obligations.
- Liability allocation.
What Is AI Regulatory Compliance for Insurers?
Quick Answer: AI regulatory compliance means ensuring that an insurer's use of AI complies with applicable insurance laws, regulations, regulatory guidance and internal governance requirements.
A compliance programme should include:
- AI inventory.
- Risk classification.
- Legal review.
- Data governance.
- Model validation.
- Fairness testing.
- Monitoring.
- Documentation.
- Vendor oversight.
What Is an AI Inventory?
Quick Answer: An AI inventory is a documented record of the AI systems used by an insurer.
It should ideally identify:
- Model name.
- Business function.
- Owner.
- Vendor.
- Data sources.
- Risk classification.
- Consumers affected.
- Validation status.
- Deployment status.
An insurer cannot effectively govern an AI system it does not know it is using.
What Is AI Model Validation?
Quick Answer: Model validation assesses whether an AI system performs appropriately for its intended purpose.
It can examine:
- Accuracy.
- Stability.
- Data quality.
- Assumptions.
- Limitations.
- Performance across relevant populations.
What Is AI Model Drift?
Quick Answer: Model drift occurs when changing circumstances cause a model's predictive performance to deteriorate.
Insurance models can be affected by:
- Economic changes.
- Climate-related risks.
- New technology.
- Fraud adaptation.
- Consumer behaviour.
Therefore, validation should not necessarily stop when a model is deployed.
Does AI Insurance Regulation Require Explainability?
Quick Answer: Explainability is an important governance consideration, but the precise legal requirement depends on the applicable law and use case.
An insurer should be able to understand sufficiently:
- What the system does.
- What data it uses.
- What risks it presents.
- How outputs affect consumers.
Explainability does not necessarily mean giving consumers proprietary source code.
Does AI Insurance Regulation Require Transparency?
Quick Answer: Transparency is an important regulatory principle, particularly where AI materially affects consumers.
Transparency can involve:
- Disclosure.
- Documentation.
- Internal accountability.
- Regulatory access.
- Consumer explanations where required.
What Is AI Consumer Protection in Insurance?
Quick Answer: AI consumer protection concerns ensuring that automated or AI-supported insurance practices do not create unlawful, misleading, inaccurate or unfair outcomes.
Potential risks include:
- Incorrect pricing.
- Unfair discrimination.
- Improper claim denial.
- False fraud alerts.
- Inaccurate external data.
- Opaque decision-making.
Can Policyholders Challenge AI Decisions?
Quick Answer: The available remedies depend on the insurance product, state law, policy terms and circumstances.
A consumer may potentially use:
- Internal complaint processes.
- Claim reconsideration.
- State insurance department complaint procedures.
- Administrative remedies.
- Private legal remedies where available.
The fact that AI was used does not necessarily create a separate cause of action.
What Is AI Market-Conduct Examination?
Quick Answer: AI market-conduct examination involves regulatory review of how an insurer uses AI in consumer-facing insurance activities.
The NAIC Market Conduct Examination Guidelines Working Group has a 2026 charge to coordinate with the Innovation, Cybersecurity and Technology Committee on examiner guidance concerning regulated entities' use of insurance and non-insurance consumer data and models using algorithms and AI. ([content.naic.org](https://content.naic.org/committees/d/market-conduct-examination-guidelines-wg?utm_source=chatgpt.com))
What Will an AI Market-Conduct Examination Examine?
Quick Answer: Potential areas include consumer data, models, governance, outcomes and compliance with applicable market-conduct requirements.
The regulatory focus is moving toward practical questions:
- What AI is being used?
- Where is the data coming from?
- Who approved the model?
- Was the model validated?
- Are consumers treated fairly?
- Are complaints revealing systematic problems?
AI Insurance Regulation Roadmap — 2026
| Regulatory Layer | Purpose |
|---|---|
| State insurance law | Binding legal requirements |
| NAIC AI Principles | Foundational framework |
| NAIC AI Model Bulletin | Responsible AI expectations |
| AI Systems Evaluation Tool | Regulatory examination support |
| Market-conduct guidance | Consumer-facing examination |
| Third-party model framework | Vendor and predictive-model oversight |
| Insurer governance | Internal AI accountability |
AI Insurance Compliance Checklist for 2026
- Create an enterprise-wide AI inventory.
- Identify every AI system affecting insurance operations.
- Classify AI systems according to risk and consumer impact.
- Identify applicable state insurance laws.
- Document each model's intended purpose.
- Identify all data sources.
- Assess external and third-party data.
- Validate material models.
- Test for accuracy and potential bias.
- Establish model-monitoring procedures.
- Monitor model drift.
- Establish human escalation procedures.
- Review third-party AI vendors.
- Maintain model documentation.
- Document material model changes.
- Monitor consumer complaints involving AI.
- Prepare for regulatory examination.
- Ensure senior management and board oversight is appropriate.
Frequently Asked Questions
Is AI regulated in the insurance industry?
Yes. AI used by insurers is subject to applicable insurance laws and regulations, alongside regulatory guidance and supervisory frameworks.
Is there a federal AI insurance law?
There is not currently one comprehensive federal statute governing every use of AI by U.S. insurers. Insurance regulation remains substantially state-based.
What is the NAIC AI Model Bulletin?
It is regulatory guidance adopted by the NAIC in December 2023 addressing responsible use and governance of AI by insurers.
Is the NAIC AI Model Bulletin legally binding?
The bulletin is not itself a federal statute. Its legal effect depends on state adoption, implementation and applicable regulatory authority.
What are the NAIC AI Principles?
The NAIC AI Principles were adopted in 2020 and provide a foundational framework for responsible AI use in insurance.
What is the AI Systems Evaluation Tool?
It is an NAIC-developed tool intended to help regulators evaluate insurer AI use, governance, risk mitigation, high-risk models and model-input data.
How many states are piloting the NAIC AI Systems Evaluation Tool?
As of March 2026, the NAIC stated that 12 participating states were piloting the tool.
Will the AI Systems Evaluation Tool become mandatory?
As of the current NAIC materials, adoption was anticipated at the 2026 Fall National Meeting. Whether and how states ultimately use the tool will depend on subsequent regulatory action.
What is the NAIC Third-Party Data and Models Working Group?
It is an NAIC working group developing a framework for regulatory oversight of third-party data and predictive models used by insurers.
What does the third-party AI framework cover?
The 2026 exposure covers third-party data and model vendors and property-and-casualty pricing and underwriting data and models.
Can insurers outsource AI compliance?
No. Outsourcing technology does not automatically eliminate the insurer's regulatory responsibilities.
Can AI insurance pricing discriminate?
Potentially. AI pricing models can create unfair or unlawful discriminatory outcomes depending on the data, model and applicable law.
Can AI deny insurance claims?
AI can support claims decisions, but the legality of automated denial depends on applicable insurance law, policy terms and claims-handling requirements.
Can AI detect insurance fraud?
Yes. AI can identify patterns, anomalies and relationships associated with potentially fraudulent claims.
Is a fraud score proof of fraud?
No. A fraud score is generally an indicator that may justify further investigation.
Does insurance AI need human oversight?
The appropriate level of human oversight depends on the AI system and its risk, but human review is particularly important for complex or high-impact consumer decisions.
What should insurers do to prepare for AI regulation?
Insurers should establish an AI inventory, risk-based governance, model validation, data controls, monitoring, third-party oversight and procedures for regulatory examination.
Conclusion
The regulation of artificial intelligence in insurance is entering a new phase.
For several years, the discussion focused primarily on a basic question:
“Should insurers be allowed to use AI?”
That is no longer the most useful question.
AI is already being used across the insurance lifecycle.
The more important question is:
“How should insurers govern AI, and how should regulators examine it?”
The U.S. answer is developing through the existing state-based insurance regulatory system.
The NAIC's AI Principles established a foundation in 2020.
The 2023 Model Bulletin translated those principles into more specific governance expectations.
By 2026, regulators are moving further toward practical supervision.
The AI Systems Evaluation Tool is designed to provide regulators with a structured way to assess insurers' AI use, governance, risk mitigation, high-risk models and input data. It was being piloted by 12 states as of March 2026. ([content.naic.org](https://content.naic.org/insurance-topics/artificial-intelligence?utm_source=chatgpt.com))
At the same time, the NAIC is addressing one of the most difficult problems in modern insurance AI:
Third-party data and models.
The Third-Party Data and Models Working Group's 2026 charge is to develop a regulatory framework for oversight of third-party data and predictive models. ([content.naic.org](https://content.naic.org/committees/h/third-party-data-models-wg?utm_source=chatgpt.com))
The framework is particularly significant because insurers increasingly rely on external providers for information that can influence:
- Underwriting.
- Pricing.
- Claims.
- Fraud detection.
The regulatory architecture can therefore be understood as a layered system:
State insurance law
+
NAIC principles and model guidance
+
Insurer AI governance
+
Model validation and monitoring
+
Third-party vendor oversight
+
Regulatory examination
+
Consumer protection.
This layered approach is important because AI is not one insurance activity.
The legal risk differs depending on whether AI is:
- Answering a customer question.
- Calculating a premium.
- Determining underwriting eligibility.
- Flagging a fraud claim.
- Recommending a claim denial.
The greater the potential impact on the consumer, the more important governance and regulatory scrutiny become.
A chatbot and an automated claim-denial system should not be treated as equivalent risks.
The 2026 regulatory direction also suggests that insurers should stop treating AI governance as merely an IT function.
AI governance is increasingly a:
Legal + actuarial + compliance + risk + technology + consumer-protection function.
For insurers, the practical message is clear:
Build the governance system before the regulator asks you to demonstrate it.
For AI vendors, another lesson follows:
If your model influences a regulated insurance decision, your technology may become part of the regulator's examination.
For policyholders, the most important principle is equally straightforward:
The presence of AI does not eliminate existing insurance rights and obligations.
Artificial intelligence may transform how insurance decisions are made.
It does not transform the fundamental regulatory principle that insurers remain accountable for the decisions made in their business.
Legal Disclaimer
This article is provided for general educational and informational purposes only. It is not legal, insurance, actuarial, financial or regulatory advice and does not create an attorney-client relationship. Insurance regulation varies by state, insurance product and individual circumstances.