Explainable AI: Why Transparency Matters in Insurance
By James W Moore, InsuranceIndustry.ai
Executive Summary
As artificial intelligence becomes increasingly embedded in insurance operations—from underwriting and pricing to claims processing and fraud detection—the ability to explain how these systems reach their decisions has emerged as both a regulatory imperative and a competitive necessity. Explainable AI (XAI) represents a fundamental shift from “black box” algorithms to transparent systems that insurance executives, regulators, and consumers can understand and trust.
Key Takeaways:
- Explainable AI makes machine learning decisions transparent and interpretable for human users
- Nearly half of U.S. states have now adopted NAIC guidance requiring insurers to address AI transparency and explainability
- Regulatory compliance, consumer trust, and risk management all depend on the ability to explain AI-driven decisions
- Insurers face growing expectations to demonstrate how AI systems avoid bias and discrimination
- Implementing XAI practices protects organizations from regulatory action while building customer confidence
What is Explainable AI?
Explainable AI refers to methods and techniques that allow human users to comprehend and trust the results produced by machine learning algorithms. Unlike traditional “black box” AI systems that provide outputs without revealing their reasoning process, XAI enables organizations to understand how a model arrived at a particular decision, what factors influenced that decision, and why specific outcomes occurred.
At its core, XAI transforms opaque algorithmic processes into transparent, interpretable insights. This means that when an AI system denies a claim, adjusts a premium, or flags a transaction as potentially fraudulent, the organization can articulate the specific factors and reasoning behind that decision in terms that regulators, customers, and internal stakeholders can understand.
The distinction between traditional AI and explainable AI isn’t merely technical—it’s fundamental to how insurance companies can deploy these technologies responsibly. A pricing algorithm might accurately predict risk, but if the insurer cannot explain why a particular applicant received a specific rate, the organization faces both regulatory exposure and erosion of customer trust.
The Regulatory Landscape: NAIC and State Adoption
The regulatory framework surrounding AI in insurance has evolved rapidly. In December 2023, the National Association of Insurance Commissioners (NAIC) adopted the Model Bulletin on the Use of Artificial Intelligence Systems by Insurers, establishing clear expectations for how insurers should govern their AI deployments. The bulletin explicitly identifies “lack of transparency and explainability” as a unique risk that AI systems can present to consumers.
The NAIC’s guidance requires insurers to implement documented AI programs that support responsible use and mitigate potential risks, including those related to transparency. As of March 2025, 24 states have adopted the NAIC Model Bulletin with little to no material changes, representing nearly half of all U.S. states. This widespread adoption signals that explainability is no longer optional—it’s becoming table stakes for operating in the insurance market.
The regulatory emphasis on explainability extends beyond the NAIC framework. State insurance departments increasingly expect insurers to demonstrate that AI-driven decisions comply with existing insurance laws, including those prohibiting unfair discrimination. Without explainability, insurers cannot effectively prove compliance or defend their decision-making processes during regulatory examinations or market conduct investigations.
Why Explainability Matters: Beyond Compliance
While regulatory compliance drives much of the current focus on explainable AI, the business case extends well beyond avoiding regulatory action. Consider these critical dimensions:
Consumer Trust and Transparency: Insurance already faces a trust deficit with many consumers who view pricing and underwriting decisions as opaque and potentially unfair. When an AI system makes decisions that impact coverage or costs, customers reasonably expect explanations. An insurer that can clearly articulate why a rate increased or why a claim was denied based on AI analysis demonstrates respect for the customer relationship and builds confidence in the fairness of the process.
Risk Management and Model Validation: Explainable AI enables insurance companies to validate that their models function as intended and identify when they might be producing problematic outcomes. If an underwriting model begins systematically declining applications from a particular demographic group, explainability tools can surface this pattern before it results in regulatory action or reputational damage. Internal audit and compliance teams need visibility into AI decision-making to fulfill their oversight responsibilities.
Competitive Differentiation: As AI adoption accelerates across the insurance industry, the ability to deploy transparent, explainable systems becomes a competitive advantage. Agents and brokers gain confidence when they can explain to clients how AI-enhanced underwriting or pricing works. Customers increasingly gravitate toward companies that demonstrate technological sophistication alongside ethical responsibility.
Fair Lending and Anti-Discrimination: For insurers offering products with financing components or operating in states with strict anti-discrimination statutes, explainability provides crucial documentation that decisions are based on legitimate risk factors rather than protected characteristics. The Consumer Financial Protection Bureau’s 2024 rule on AI-based home appraisals emphasizes the need for fairness and adverse-action explanations—a principle that extends to insurance applications.
Practical Applications in Insurance Operations
Explainable AI manifests differently across various insurance functions, each with unique transparency requirements:
Underwriting and Pricing: When AI systems evaluate risk and determine premiums, explainability allows underwriters to understand which factors most heavily influenced the decision. This transparency helps insurers ensure that pricing reflects genuine risk correlations rather than spurious patterns or proxy discrimination. It also enables insurers to provide meaningful adverse action notices when required.
Claims Processing: Automated claims systems benefit from explainability by allowing adjusters to understand why certain claims were flagged for additional review or why settlement recommendations fell within specific ranges. This transparency supports consistent decision-making and helps identify when AI systems might be missing important context that human judgment would capture.
Fraud Detection: While explainability in fraud detection requires careful balance to avoid revealing detection methods to bad actors, insurers still need internal transparency about what patterns trigger fraud alerts. This allows fraud investigators to validate that the system identifies genuine fraud indicators rather than producing false positives that frustrate legitimate customers.
Customer Service and Distribution: AI-powered chatbots and recommendation engines increasingly interact with customers and agents. Explainability in these contexts means the technology can articulate why it suggested particular coverage options or how it arrived at specific guidance, building trust in the technology’s recommendations.
Implementation Considerations for Insurance Leaders
Adopting explainable AI requires thoughtful planning and coordination across technology, compliance, and business functions. Insurance executives should consider several key dimensions:
Governance Structure: The NAIC Model Bulletin requires documented AI programs with clear accountability. This means establishing governance frameworks that define roles and responsibilities for AI oversight, including who evaluates model explainability and how explanations are validated for accuracy and completeness.
Vendor Management: Many insurers rely on third-party AI solutions for underwriting, claims, or fraud detection. The NAIC guidance explicitly addresses third-party arrangements, noting that insurers should implement contract terms allowing audit rights and requiring vendor cooperation with regulatory inquiries. Insurance executives must ensure their vendor agreements include provisions for explainability and transparency, not just performance metrics.
Technology Architecture: Some AI techniques are inherently more explainable than others. Deep neural networks often function as black boxes, while decision trees and certain ensemble methods provide clearer insight into their reasoning. Insurance technology leaders must balance the performance advantages of complex models against the transparency benefits of more interpretable approaches.
Training and Culture: Explainability isn’t purely technical—it requires insurance professionals who can interpret and communicate AI-driven insights. This means investing in training for underwriters, claims adjusters, and customer service representatives who need to explain AI-influenced decisions to consumers and regulators.
Documentation and Auditability: Regulators may request detailed information about AI development and deployment, including governance practices, risk management approaches, and internal controls. Insurance companies need systems that document not just what decisions were made, but how the AI arrived at those decisions and what validation occurred.
The Path Forward
AI continues transforming insurance operations, with both traditional machine learning methods and newer generative AI applications finding increased adoption across the industry. This technological evolution brings tremendous opportunity for improved efficiency, enhanced customer experience, and more accurate risk assessment. However, these benefits can only be fully realized when insurers implement AI systems that are transparent, explainable, and aligned with regulatory expectations.
The convergence of regulatory requirements, consumer expectations, and risk management imperatives makes explainable AI not just a compliance checkbox but a strategic priority. Insurance executives who view explainability as foundational to their AI strategy—rather than an afterthought—position their organizations for sustainable competitive advantage in an increasingly AI-driven market.
Action Items for Insurance Executives
Assess Current State: Inventory existing AI systems across your organization and evaluate the extent to which each can explain its decisions. Identify gaps where explainability needs strengthening.
Review Vendor Agreements: Examine contracts with third-party AI providers to ensure they include appropriate provisions for transparency, explainability, and regulatory cooperation.
Establish Governance: If not already in place, develop a documented AI program that addresses the NAIC Model Bulletin requirements, including specific protocols for ensuring and validating explainability.
Invest in Capabilities: Build internal expertise in explainable AI techniques and ensure that business teams understand how to interpret and communicate AI-driven insights.
Engage Stakeholders: Create forums for dialogue between technology teams, compliance functions, business leaders, and legal counsel about explainability requirements and implementation approaches.
Monitor the Regulatory Landscape: Stay informed about evolving state and federal requirements around AI transparency, as additional states continue adopting the NAIC guidance and new regulations emerge.
Sources
National Association of Insurance Commissioners. “NAIC Model Bulletin: Use of Artificial Intelligence Systems by Insurers.” December 2023. https://content.naic.org/sites/default/files/inline-files/2023-12-4%20Model%20Bulletin_Adopted_0.pdf
Quarles & Brady LLP. “Nearly Half of States Have Now Adopted NAIC Model Bulletin on Insurers’ Use of AI.” March 2025. https://www.quarles.com/newsroom/publications/nearly-half-of-states-have-now-adopted-naic-model-bulletin-on-insurers-use-of-ai
Norton Rose Fulbright. “Key regulatory developments around AI insurers should be aware of in 2025.” https://www.nortonrosefulbright.com/en/knowledge/publications/5accc826/ai-and-the-insurance-sector-balancing-benefits-with-regulatory-complexity
Fenwick & West LLP. “AI in the Insurance Industry: Balancing Innovation and Governance in 2025.” February 13, 2025. https://www.fenwick.com/insights/publications/ai-in-the-insurance-industry-balancing-innovation-and-governance-in-2025
IBM. “What is Explainable AI (XAI)?” IBM Think Topics. https://www.ibm.com/think/topics/explainable-ai
Baker Tilly. “The regulatory implications of AI and ML for the insurance industry.” https://www.bakertilly.com/insights/the-regulatory-implications-of-ai-and-ml-for-the-insurance-industry
Kennedys Law. “Understanding the NAIC model AI bulletin: what it means for insurers.” January 21, 2025. https://www.kennedyslaw.com/en/thought-leadership/article/2025/understanding-the-naic-model-ai-bulletin-what-it-means-for-insurers/
Gradient AI. “What’s Next for AI in Insurance? 6 Trends to Watch in 2025.” April 17, 2025. https://www.gradientai.com/pc-blog-whats-next-for-ai-in-insurance-6-trends-to-watch-in-2025
About the Author: James W Moore brings over 40 years of insurance industry experience spanning carriers, agencies, and wholesalers, combined with expertise in IT management and digital innovation. As founder of InsuranceIndustry.ai, he provides thought leadership on artificial intelligence applications in insurance.
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