The ASTIN Reading Club will be focusing in the first part of 2024 on Climate Change and its impacts on non-life insurance. The first talk in this series will be on claims modelling with climate data.

General insurance pricing often involves the use of statistical techniques to estimate the expected claims cost associated with a relevant risk profile. These techniques include GLMs and policyholder specific rating factors. 

In this discussion:

• Provides a framework for thinking about modelling climate-related risks, then deep-dives into how the pricing process can be improved
• Directly links an exposure dataset of buildings risk and associated claims experience to a high-resolution gridded precipitation dataset to investigate the predictive power of this feature on both an actual and forecasted basis
• Establishes a modelling framework that allows for the estimation of both the frequency and severity of buildings claims given the combined dataset
• Considers the relative importance of the added precipitation feature against traditionally used rating factors and the sensitivity of the underlying claims frequency and severity to changes in precipitation
• Finally, by considering different precipitation scenarios, we show how the risks of excessive precipitation can be quantified, allowing for more accurate forecasts of financial performance to be made, and risk mitigation strategies to be investigated.