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Cet article analyse une recherche qui présente SwiGAN, un cadre d'intelligence artificielle conçu pour simuler l'évolution des risques climatiques, particulièrement la sécheresse géotechnique en France jusqu'en 2050. En s'appuyant sur des réseaux antagonistes génératifs (GAN) de type Wasserstein, les auteurs modélisent l'indice d'humidité des sols afin d'anticiper les futurs dommages liés à la subsidence.

This report examines how European (re)insurers address biodiversity risks, which threaten financial stability due to their complexity and links with climate risks. Despite challenges in quantifying impacts, one in five insurers references biodiversity in their risk assessments, though mostly qualitatively. Promising practices show growing awareness, but regional variations and limited metrics hinder progress. EIOPA calls for enhanced collaboration to improve data, models, and risk management, emphasizing the need to better understand the climate-biodiversity nexus and explore nature-based solutions to address insurance gaps.

"... model uncertainty is a vital component of the current challenges in risk measurement, and therefore the regulator should design risk measures encouraging well-understood prudent decisions over (less understood) risky ones. From this perspective robust regulation should be a desirable goal. To achieve such an objective, simple – but not simpler – rules are needed."

"... we propose an approach to estimate very large losses similar to that used by Fermi and Drake to estimate the existence of extraterrestrial life. It consists of supposing the event of interest is the result of a concatenation of independent factors and estimating the probability of each factor. The problem is that the events in the causal chain might be events that have never been observed, which ties our subject to that of the estimation of probabilities of rare events."

"This paper extends the traditional multi-state models to include epidemic effects."

"Organizations closest to full adoption are those under the prudential regulation (coercive forces), whereas efficiency motives and mimetic forces drive organizations to seek fluidity by ‘blending’ the segregated lines to ensure fast reactions to changing environment."

"An efficient Bayesian Markov Chain Monte-Carlo method is developed to estimate the unknown parameters to address the computational complexity. Our empirical application to the mortality data collected for the Group of Seven (G7) nations demonstrates the efficacy of our approach."

"These parameters can be calibrated using public data. This new approach means not only to evaluate climate risks without picking any specific scenario but also allows to fill the gap between current one year approach of regulatory and economic capital models and the necessarily long-term view of climate risks by designing a framework to evaluate the resulting credit loss on each step (typically yearly) of the transition path. This new approach could prove instrumental in the 2022 context of central banks weighing the pros and cons of a climate capital charge."

"Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis-Hastings algorithm. Predictions and applications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed."

"We extend the scope of risk measures for which backtesting models are available by proposing a multinomial backtesting method for general distortion risk measures. The method relies on a stratification and randomization of risk levels. We illustrate the performance of our methods in numerical case studies."