Artificial intelligence (AI) is fundamentally reshaping the insurance industry. From predictive analytics that refine underwriting processes to automated systems revolutionizing claims processing, AI unlocks the promise of operational excellence, elevated customer experiences, and robust risk management. Yet, as these technologies proliferate, regulatory frameworks have struggled to keep pace, raising complex questions about legal standards, ethical boundaries, and consumer protection.
Recently, these questions have taken center stage with President Trump's “One Big Beautiful Bill Act,” which seeks to impose a 10-year moratorium on state regulation of AI, shifting oversight exclusively to the federal level.
This unprecedented move has drawn considerable criticism from key stakeholders, especially the National Association of Insurance Commissioners (NAIC)1 and the Professional Insurance Agents (PIA).2 Both organizations argue that undermining state authority puts consumers and the insurance market at risk, thrusting the industry into uncertainty and challenging the established regulatory order.
This blog will explore the multifaceted role of AI in insurance, weighing both the benefits and the risks, and will dissect the implications of the proposed bill. We’ll look closely at the NAIC and PIA's objections and draw connections to real-world case studies, before concluding with a forward-looking perspective on the future of AI regulation within the sector.
The insurance industry has long embraced data analytics, predictive analytics, machine learning, and now AI as strategic enablers of innovation and efficiency. In brief, those benefits include:
Operational Efficiency: AI systems streamline repetitive tasks such as claims triage, fraud detection, and policy reviews and accelerate technology integrations. Predictive models allow companies to identify risky claims more accurately, reducing investigation time and saving costs. These advancements are not theoretical. According to research by Digital Insurance (Arizent), more than half of insurers already are executing AI deployments and those in production are producing results in line with expectations.3
Enhanced Customer Experience: AI-powered chatbots and virtual assistants have revolutionized how customers interact with their insurers. They provide quick, round-the-clock support, resulting in better response times and higher customer satisfaction rates. Personalization capabilities enabled by machine learning also enable insurers to offer tailored products that meet individual policyholder needs.
Improved Risk Management: Predictive analytics and natural language reporting allow insurers to assess and price risks more accurately. For example, data-driven algorithms can forecast potential losses due to natural disasters or identify underwriting trends that minimize exposure. These use cases underline AI’s ability to create more resilient insurance businesses.
While AI's allure is clear, its adoption introduces potential challenges that cannot be ignored, such as:
Bias in Decision Making: Algorithms are only as impartial as the data they're trained on. If historical data contain biases, intentional or unintentional, AI-driven processes can perpetuate those same disparities. For instance, biased inputs could lead to discriminatory practices in underwriting or claims approvals.
Data Privacy and Security Concerns: Where AI systems intersect with consumer data, security and privacy must be retained. Without safeguards, sensitive customer data may be at risk of breaches or misuse.
Lack of Transparency: AI models can operate as "black boxes," where decision logic is opaque. This lack of explainability makes it difficult for consumers and regulators to understand or challenge decisions, diminishing trust.
To better understand the current controversy, we must examine the bill at the heart of the debate. The "One Big Beautiful Bill" proposes to centralize AI oversight by prohibiting state legislatures and regulators from introducing or enforcing any AI-specific laws for 10 years. The rationale behind this provision lies in supporting innovation by providing a unified regulatory framework and eliminating potential friction from varying state approaches, NAIC says.4
Historically, however, the insurance industry has operated under a state-based regulatory system codified by the McCarran-Ferguson Act of 1945. This model allows each state to develop tailored frameworks that reflect local market conditions and consumer needs. By contrast, federal preemption raises concerns about overly generic policies that fail to address the nuanced challenges faced by insurers in specific jurisdictions, according to NAIC.5
The proposed shift has not gone uncontested. Both NAIC and PIA have voiced concerns that merit close attention. One of the NAIC's primary concerns is that the moratorium undermines its ability to protect consumers effectively.
State regulators have traditionally played a hands-on role in overseeing insurers’ practices to ensure fairness and transparency. A federal approach, they argue, may lack the agility to swiftly address localized issues.
A further objection from both the NAIC and PIA centers on the bill’s overly expansive definition of AI. They state the language is so broad that it could inadvertently encompass routine analytical tools, potentially including advanced machine learning systems, as well as "a wide range of processes using existing analytical tools and software that insurers rely on every day, including calculations, simulations, and stochastic forecasts performed in millions if not hundreds of millions of Excel spreadsheets, databases, coded scripts, and a multitude of InsurTech provided analytical systems for rate setting, underwriting, and claims processing. This sweeping approach could prevent state regulators from reviewing or responding to new risks in these areas, even when no actual ‘artificial intelligence’ is involved,” according to NAIC.6
This imprecision risks stifling essential oversight functions, even in non-AI contexts, potentially leaving consumers exposed to harms such as data misuse or algorithmic bias. State regulators also fear that limiting their authority will discourage innovation by creating regulatory uncertainty and could disrupt established processes for reviewing AI models, leading to delays in implementing protections, which could erode public confidence in the insurance industry.
As the legislative debate continues, the future of AI regulation in insurance stands at a crossroads, with at least two possible outcomes from the bill:
Insurers and regulators must strive for a middle ground that balances innovation with accountability. Partnerships between state and federal authorities could streamline oversight while retaining the adaptability needed to address localized challenges. Meanwhile, insurers can take proactive steps to adopt ethical AI principles, positioning themselves as leaders in responsible innovation.
The NAIC offers a strategy for ethical AI adoption, including:
The Insurance Information Institute, in collaboration with data and AI solutions provider SAS, also offers a framework to help insurers establish best practices8 before stricter regulations mandate them. According to the institute, "the model addresses Oversight, Operations, Compliance, and Culture aspects of an organization." This collaborative governance approach is designed to help anticipate, mitigate, and avoid unintentional harm, particularly for the most vulnerable.
AI's integration into the insurance industry is as inevitable as its potential for transformation. While the "One Big Beautiful Bill" seeks to streamline oversight, it raises pressing concerns about the effectiveness of a one-size-fits-all federal framework. By examining these issues through the lens of the NAIC and PIA's objections, it becomes clear that the insurance sector needs a balanced regulatory approach — one that fosters innovation without compromising consumer trust.
As of August 5, 2025, 24 jurisdictions had adopted NAIC’s December 2023 AI model bulletin, and Colorado, New York, California and Texas had devised their own state-issued guidance for insurers. The NAIC also is developing a tool to help regulators evaluate risks related to insurers' use of AI, according to reports from S&P Global.9
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