Working paper
Working paper · 2022
(Previously titled "Borrowing Information Across Space and Time: Pricing Flood Risk with Physics-based Hierarchical Machine Learning Models")
Abstract
Flood risk is an increasingly urgent concern as climate change intensifies the frequency and severity of extreme events. Flood insurance is a key tool to protect against such risk, but its modeling and pricing remain challenging. This paper proposes a Geo-Hierarchical Deep Learning (GHDL) framework, a scalable, interpretable, and cost-efficient approach, to generate high-resolution flood hazard factors and inform insurance pricing. The GHDL framework can effectively capture spatial dependencies that are essential for an accurate flood risk assessment by incorporating geographical connectivity directly into deep learning models. Using data from the Mississippi River Basin, we show that GHDL achieves 86%–91% accuracy in extreme scenarios, outperforming climate-uninformed and benchmark deep learning models. Applied to the National Flood Insurance Program (NFIP), GHDL-derived hazard factors reduce net premiums by 33.5% and solvency capital requirements by 32.3%. These improvements enhance pricing adequacy and contribute to the long-term sustainability of public flood insurance programs.
Keywords: Flood risk, Climate risk, Deep learning, Ratemaking, Colvency capital requirement.