In a context of widespread instability, the actuarial analysis of emerging risks takes on particular importance to facilitate the adaptation of insurance to these profound changes. Whether it be old risks with poorly anticipated consequences (such as the epidemiological risk, as demonstrated by the recent Covid-19 crisis, whose consequences can significantly exceed purely healthcare boundaries), risks undergoing strong and worrying evolution (such as climate risk), or more recent risks (such as cyber risk), the task of actuaries is particularly delicate. The lack of data is notably a hindrance to a rigorous approach to these important questions. This session aims to lay the groundwork for a scientific approach to this problem and to introduce some tools for discussing the insurability of these new risks.
This session will notably have two main angles. Firstly, it will raise the question of data, their quality, and the necessary reliance on expert judgment, crucial for risks where experience is limited. Bayesian analysis notably allows for this integration, but does not exempt from a critical examination of the quality of expert data, and we will thus present some methods to attempt to evaluate it. Secondly, we will examine the shift from a 'historical data' approach to a "scenario-based" approach. Because while the notion of scenario allows for anticipating situations never encountered before, their design must adhere to scientific standards to avoid being purely speculative.
A simplified illustration of these issues will be provided during the training. An R notebook will allow participants to follow and adapt the implementation without needing an in-depth knowledge of the software.
Early-bird discount is available for bookings made by 2 October 2025.
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