AI-Enabled Underwriting Brings New Challenges for Life Insurance: Policy and Regulatory Considerations

Abstract

Insurers are increasingly using novel data sources and automated systems for risk classification and underwriting. Automation has improved operational efficiencies in the accuracy and speed of underwriting, but it also raises new considerations relating to unfair discrimination. In this paper, we review the current regulatory structures relating to unfair discrimination and suggest they are insufficient to police the myriad new big data sources available. Moreover, AI-enabled systems increase the risk of unfair discrimination if a facially neutral factor is utilized by an automated system as a proxy for a prohibited characteristic. Furthermore, many insurers rely on unregulated third-party algorithm developers, and therefore do not own and may not have access to the logic embedded in the system, which raises unique ethical implications, particularly with respect to accountability among AI actors. To address these issues, we propose a framework that consists of three parts: (a) the establishment of national standards to serve as guardrails for acceptable design and behavior of AI-enabled systems; (b) a certification system that attests that an AI-enabled system was developed in accordance with those standards; and (c) periodic audits of the systems’ output to ensure it operated consistent with those standards. The framework rests on the existing state-based regulatory infrastructure and envisions a self-regulatory organization who can work with the NAIC to develop standards and oversee certification and audit processes. Regulatory enforcement remains with the states. Part I describes the use of technology in life insurance underwriting. Part II discusses the unfair discrimination that can occur due to factors that reflect societal biases, and the unfair discrimination that could occur in artificially intelligent systems if facially neutral factors are substituted by the system for prohibited factors. The current industry standards and regulatory scheme for unfair discrimination in underwriting is also discussed in Part II. Part III describes the ethical concerns regarding accountability when third-party data inputs and underwriting systems are utilized. In Part IV, we propose a governance approach and framework to address these concerns.