Brochures
May 27, 2025
A global stochastic drought model for (re)insurance
At Reask, we have developed a globally connected, climate-driven stochastic drought model to support hazard assessment in the (re)insurance industry.
This model uses machine learning and dimensionality reduction techniques to identify and drive key modes of drought variability, according to large-scale climate signals. By conditioning the drought state to historical climate signals, we can generate stochastic drought event sets at both global and regional scales.
The model’s seamless merging of regions ensures consistency across continents, even when averaged over multiple samples.
Key takeaways:
Climate-Driven Drought Modeling: Our stochastic model links large-scale climate signals to regional and global drought conditions, ensuring a dynamic, climate-responsive approach to risk assessment.
Global and Regional Drought Hazard Assessment:The model is configured in such a way that many different combinations of drought indexes can be merged, allowing drought affecting different lines of business (e.g. commercial, residential, and agricultural) to be understood.
Seamless Regional Integration: The model integrates regional data with global climate patterns, ensuring smooth transitions across continental borders, enhancing the model’s applicability for worldwide drought risk management.
This globally connected stochastic model can be used as an effective tool in understanding the growing threat of drought, supporting better decision-making in sectors affected by drought conditions globally.
