Catastrophe Insurance Pricing

C. Zeng and D. Bertsimas, Catastrophe Insurance Pricing: A Robust Optimization Approach, In preparation for Management Science, 2023. 

The escalating frequency and severity of natural disasters, exacerbated by climate change, underscore the critical role of insurance in facilitating recovery and promoting investments in risk reduction. This work introduces a novel Robust Optimization (RO) framework tailored for the calculation of catastrophe insurance premiums, with a case study applied to the United States National Flood Insurance Program (NFIP). To the best of our knowledge, it is the first time a RO approach has been applied to for disaster insurance pricing. Our methodology is designed to protect against both historical and emerging risks, the latter predicted by advanced machine learning models, thus directly incorporating amplified risks induced by climate change. The framework offers three key contributions: first, using the US flood insurance data as an example, our model demonstrates a robust capacity to cover losses and produce surpluses, with a smooth balance transition through parameter fine-tuning; second, it introduces an equitable premium pricing structure, aligning higher premiums with regions of heightened risk; third, the framework offers versatility and generalizability, making it adaptable to a variety of natural disaster scenarios, such as wildfires, droughts, etc. This work not only advances the field of insurance premium modeling but also serves as a vital tool for policymakers and stakeholders in building resilience to the growing risks of natural catastrophes.