Events

There is mounting pressure on (re)insurers to quantify the impacts of climate change, notably on the frequency and severity of claims due to weather events such as flooding. This is however a very challenging task for (re)insurers as it requires modeling at the scale of a portfolio and at a high enough spatial resolution to incorporate local climate change effects.

In this webinar, we introduce a data science approach to climate change risk assessment of pluvial flooding for insurance portfolios over Canada and the United States. The underlying flood occurrence model quantifies the financial impacts of short-term precipitation dynamics under present and future climate conditions by leveraging statistical, machine learning, and climate model data. The model is designed for applications that do not require street-level precision, as is often the case for scenario and trend analyses.

Our analyses show that climate change and urbanization will typically increase losses across Canada and the United States, while impacts remain strongly heterogeneous across regions. Portfolio applications highlight the importance for (re)insurers to differentiate between future changes in hazard and exposure, as the latter may magnify or attenuate the impacts of climate change on losses.

This webinar is based on a paper published in the ASTIN Bulletin.

PBSS: Unlock how Artificial Intelligence is reshaping pensions and social security work. Join us for an insightful session featuring leading actuarial AI expert Ronald Richman, who will share practical applications of AI in longevity modelling, experience analysis, and actuarial workflow automation. This webinar will highlight real-world use insights, emerging opportunities, and what actuaries doing work in pensions and social security need to know to stay ahead in an AI‑driven future. Don’t miss this opportunity to learn from one of the field’s most influential voices.

Motivated by insurance applications, we propose a new approach for the validation of real-world economic scenarios. This approach is based on the statistical test developed by Chevyrev and Oberhauser (2022) and relies on the notions of signature and maximum mean distance. This test allows to check whether two samples of stochastic processes paths come from the same distribution. Our contribution is to apply this test to a variety of stochastic processes exhibiting different pathwise properties (Hölder regularity, autocorrelation, and regime switches) and which are relevant for the modelling of stock prices and stock volatility as well as of inflation in view of actuarial applications.