Navigating Insurance Challenges: A Decision Tree Model for Insurance Pricing and Risk Strategy

Abstract

Amid escalating natural disasters intensified by climate change, accurately assessing risks and determining insurance rates in disaster-prone areas present significant challenges to the insurance industry, impacting the underwriting decisions and strategies correspondingly. This article employs a frequency-loss degree framework to quantify meteorological disaster risk as the Annual Average Loss (AAL), which was classified into three categories (severe, moderate, and mild) further. Utilizing a Poisson Distribution to process extreme event data and integrating it with data from typical years into a comprehensive prediction model, this approach facilitates enhanced risk assessment and premium estimation. A decision tree model was further established to explore insurance institution’s underwriting decisions and risk strategies on the basis of monetary benefits and mental utility respectively, highlighted by case studies in Australia and the Philippines. The findings underscore the necessity for insurers to incorporate advanced decision-making tools taking utility factors into account, to navigate the complexities of risk and pricing in vulnerable areas efficiently. A tendency was observed among insurers to prefer conservative strategies in medium-risk areas, like the Philippines, over aggressive underwriting in high-risk zones, such as Western Australia. This suggests further research into expanding the model's applicability and delving into the evolving influence of climate change on the insurance industry.