From Manual Construction to AI-Driven Scenario Emergence: Rethinking Catastrophe Risk Modeling

A study posted to arXiv (2609.16493v1) proposes the TAISE framework, which repurposes AI weather forecasting models to generate coherent extreme weather sequences for catastrophe risk modeling at an order-of-magnitude lower computational cost than conventional methods. The proof-of-concept experiment produced continuous global atmospheric fields with temporal continuity and cross-regional correlations that snapshot-based approaches lack, addressing a manual construction process largely unchanged since the 1990s. The authors say the approach could democratize catastrophe risk quantification for insurers, reinsurers, ILS fund managers and public-sector risk managers.

arXiv:2609.16493v1 Announce Type: new 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.