AI transforms catastrophe risk modeling with cheaper weather scenarios
From Manual Construction to AI-Driven Scenario Emergence: Rethinking Catastrophe Risk Modeling
Artificial IntelligenceMachine Learning
Summary
Traditional ways to predict extreme weather for insurance and risk management are slow and expensive. The authors propose using AI models that forecast weather to generate continuous and realistic sequences of extreme weather at a much lower cost. Their method keeps track of how weather events change over time and connect across regions, which older methods miss. This could make it easier and faster for insurers and government agencies to measure and prepare for disaster risks.
What this means in practice
- •For insurance analysts: Produce cheaper and more continuous extreme weather scenarios to improve catastrophe risk assessments across large insurance portfolios.
- •For public-sector risk managers: Generate dynamic and regionally connected extreme event simulations to assist in planning and disaster preparedness.
Authors
Hang Gao
Abstract
Traditional catastrophe (CAT) risk models rely on costly manual construction to generate extreme weather scenarios, an approach largely unchanged since the 1990s. As climate extremes intensify, this creates mounting challenges to the entire risk transfer chain. This study proposes the TAISE framework, which repurposes AI weather forecasting models to produce coherent extreme weather sequences at a fraction of traditional costs. Through self-iterative generation, the framework produces continuous global atmospheric fields from which extreme events emerge. A proof-of-concept experiment demonstrates an order-of-magnitude reduction in computational cost compared with conventional methods, while capturing temporal continuity and cross-regional correlations absent in snapshot-based approaches. These findings suggest a pathway toward democratising catastrophe risk quantification and enabling dynamic, comprehensive portfolio assessment for insurers, reinsurers, ILS fund managers and public-sector risk managers.