Explainable AI (XAI) for Health Insurance Underwriting

Abstract

This research paper focuses on offering an understanding of the role of Explainable Artificial Intelligence (XAI) when underwriting a health insurance policy. The latter is due to AI systems being applied in risk management and policy-making across the insurance industry, and hence, there is a rising need for explicability of such systems. This work provides a detailed exploration of several XAI methods, the application of the selected approaches within the case of health insurance, and the barriers to achieving a proper level of model interpretability and reliability. We focus on the national and international methods for interpretation, the locally interpretable deep learning models, the metrics for XAI in underwriting context. Furthermore, we present a brief of important regulatory concerns, ethical issues, and recommendations for further research wherein the field is experiencing rapid expansion. Based on our findings, we conclude that XAI provides viable solutions for building trustworthiness in health insurance underwriting AI systems but state limitations with relation to scalability for handling complex health data and to meet strict regulatory concerns. The study therefore presents XAI as a tool that has the potential of transforming underwriting process especially by improving the amount of trust that health insurance markets and consumers place in the AI models used in underwriting.