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

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.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2609.16493 [cs.AI]

(or arXiv:2609.16493v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.16493 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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