How AI regulation is evolving in the insurance industry


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Earlier this week I was in San Diego as a speaker and guest of the National Association of Insurance Commissioners (NAIC) National meeting. I had the opportunity to share some of my own perspectives and opinions with the Big Data and Artificial Intelligence working group. I have also had the opportunity to participate in meetings with key stakeholders involved in reviewing the next steps towards regulatory oversight of AI.

2021 has seen a significant acceleration in regulatory interest and posture regarding the use of AI, both in the insurance arena and more broadly. From the New York City Council creating legislation to master AI biases during the hiring process to the Federal Trade Commission guidelines On how to create and deploy responsible AI and machine learning models, governing bodies in the United States have shown a direct interest in regulating AI. For insurance companies with European exposure, a just released an update to the proposed EU AI law now specifically places the use of AI in the insurance industry in the “high risk” category.

In August 2020, the NAIC brought to the fore the principles of AI. Over the past year, his goal has been to gain more data on the exact state of the insurance industry in its use of AI. The priority was to get an idea of ​​the impact of regulations on the use of AI technologies by industry. During the Big Data Working Group, a first public overview was offered on the results of an investigation of damage carriers and their use of AI. The results show wide application of AI in the core functions of this group of insurers. This task force appears likely to expand the survey to owners and life insurance lines of business in the coming months.

The challenge of regulating AI is not trivial. Regulators need to strike a balance between protecting consumers and supporting innovation. Several themes are evident regarding regulatory perspectives on the use of AI in insurance:

  • An appreciation that AI is a complex system resulting from actions, decisions and data driven by a team of stakeholders throughout the lifecycle of a system.
  • Understanding that regulations will need to include evidence of comprehensive lifecycle governance and objective reviews of key risk management practices.
  • Agreement among regulators that they are largely not equipped to perform, with state regulatory staff, in-depth technical reviews or forensic analyzes of AI systems. To be successful in regulatory oversight, they need more training, partnerships with more specialized organizations, and a degree of demonstrated accountability from carriers going forward.
  • A possibility that the substantive regulations that shape and define regulations will have to be forged at the federal level – and not just at the level of state-level insurance departments.

Looking back on my conversations in San Diego – and throughout the year – I have another point to think about: we could all benefit from being more direct. Where does the specific AI regulation start or end? How should insurance companies fundamentally change to better serve the often underserved categories of our population?

My career has not been in insurance. However, I very quickly realized that many of the conversations about fairness and bias in AI governance venues are by no means exclusive to AI governance. Instead, these are more important questions and considerations about balancing the appropriate risk assessment factors and the correlation these factors may have with fair treatment of certain classes of our population. I 100% agree that we have economic disparities and inequalities, and I want to see more inclusive markets; However, I would hate to see important and much needed governance practices that enhance key principles like transparency, security and accountability to wait for agreements on, in my opinion, the much larger and more difficult discussions about fairness.

I have constantly heard from regulators and industry stakeholders in San Diego that insurance is experiencing a technological renaissance. There seems to be a consensus that the way regulation works today is not what we need regulation of in the future. In some ways, improving the NAIC’s focus on AI through the creation of a new “letters committee” at the highest level (H) – only the eighth such committee in the 150-year history of the NAIC – is a tremendous acknowledgment of this reality.

The next year will provide a more in-depth perspective on insurance regulators’ approach to the use of AI. We’ll see Colorado further define the practices and plans for SB21-169: Restrict the use by insurers of external consumer data. We will likely see developments in federal policy or legislation that might even look like HR 5596: the Justice Against Malicious Algorithms Act, 2021.

What should carriers be doing right now with all these moving parts? At a bare minimum, insurance companies should internally organize key stakeholders related to AI strategy and development to collaboratively assess how they define and develop AI projects and models. If carriers haven’t yet put in place extensive lifecycle governance or risk management practices specific to their AI / machine learning systems, they should start this journey in a hurry.

Antoine Habayeb is founding CEO of Monitaure, an AI governance and ML insurance company.

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