Loss Assessment and Earthquake Parametric Insurance Pricing Using Hybrid Bayesian Network

Assessing the economic losses caused by earthquakes is important for disaster relief and post-disaster loss compensation. As casualty information becomes available, it supports detailed loss estimations in accurately determining damages and facilitating economic compensation. Parametric insurance has emerged as a crucial tool for mitigating risk and compensating catastrophic losses with the benefits of simplified administration and expedited payment processes. Yet, its development is impeded by basis risk, which refers to the risk that the insurance payout does not match the actual loss. This paper introduces a hybrid Bayesian network (HBN) model to construct both a rapid loss assessment model and a post-disaster loss estimation model for earthquakes. The key difference between the two models is the integration of casualty data in the post-disaster assessment model. Furthermore, an innovative dual-parameter insurance pricing model is proposed, utilizing both magnitude and epicenter intensity-defined as the damage in the most affected area of an earthquake. This study also includes a practical application, calculating insurance premiums for Dali Prefecture in Yunnan Province. The research findings demonstrate that the HBN model outperforms traditional parametric methods, multiple linear regression (MLR), and random forest (RF) regarding predictive accuracy and interpretability. Additionally, integrating casualty data further enhances the model's predictive accuracy. The insurance premium calculations based on the dual-parameter model provide practical guidance for implementation. The accuracy of the model's predictions can significantly reduce basis risk. The paper contributes to more effective disaster response strategies and risk management practices, offering valuable insights for improving earthquake resilience and preparedness.