Sarcouncil Journal of Economics and Business Management

Abstract: The health insurance industry in the U.S. is increasingly exposed to systemic financial risks derived from higher healthcare spending levels, demographic changes, technology improvements, and correlated health shocks such as pandemics. Traditional actuarial models and methods, which concentrate on individual risk and insurance company solvency, often do not cover the entire 'market'. Actuarial science, however, provides us with very powerful tools to quantify uncertainty and to predict financial charges, and we can build robust risk-sharing mechanisms accordingly. In this paper, we develop a framework for addressing systemic risk within healthcare insurance by bringing together fundamental actuarial techniques, such as stress testing, dependence modeling, dynamic pricing, and reinsurance pricing. The framework focuses on three areas: ability to withstand large system-wide shocks, financial sustainability of the insurer, and ability to rapidly adapt to new healthcare risks. Contributions include a systemic risk management conceptual model, suggestions for regulation integration (via systemic risk reserves or mandatory stress testing), and applications in enterprise risk management and value-based care pricing. Future research directions, such as empirical verification, actuarial-based AI regulatory modeling, and use of AI-improved actuarial methods are also suggested.