Resilience in Property Insurance: A Path to Sustainability

This study addresses the challenge of extreme weather on property insurance, proposing a model that balances insurer profitability and affordability for owners. By integrating strategic underwriting, normalized data, and advanced forecasting, the model aims to enhance system resilience and close the Insurance Protection Gap, particularly in high-risk areas like the U.S. and Europe, which are increasingly affected by natural disasters. To achieve these goals, we employ state-of-the-art computer technologies, including 3D scene analysis for enhanced visualization and assessment of property risks, as well as machine learning algorithms for time series forecasting. These techniques, along with least squares optimization and robustness checks, allow for more accurate risk evaluation and support the development of sustainable underwriting practices.