Machine Learning-Powered Actuarial Science: Revolutionizing Underwriting and Policy Pricing for Enhanced Predictive Analytics in Life and Health Insurance - Lahari Pandiri, Subrahmanyasarma Chitta (South Eastern European Journal of Public Health, 2024)
ABSTRACT The integration of machine learning (ML) techniques into actuarial science is transforming the landscape of life and health insurance by enhancing predictive analytics, underwriting processes, and policy pricing models. This paper explores the potential of machine learning to revolutionize actuarial practices, offering improved accuracy, efficiency, and scalability. Traditional actuarial methods, which rely heavily on historical data and statistical models, are increasingly supplemented by ML algorithms capable of analyzing vast and complex datasets, uncovering hidden patterns, and making real-time predictions. By harnessing advanced ML techniques such as supervised learning, neural networks, and natural language processing, insurers can refine risk assessment models, optimize policy pricing, and personalize underwriting decisions. The paper also discusses the implications of these advancements for actuarial professionals, including the shift toward more data-driven, automated workflows, and the ethical considerations surrounding data privacy and algorithmic bias. Ultimately, ML-powered actuarial science promises to usher in a new era of precision in risk management and a more dynamic approach to insurance operations, benefiting both insurers and policyholders alike.