Monte Carlo guided Diffusion for Bayesian linear inverse problems
De Sylvain Le Corff
Optimal revelated utilities and convex pricing kernels:a forward point of view of convexity propagation
De Nicole El Karoui
Apparaît dans la collection : Machine Learning in Insurance Sector Targeted to Risk Analysis and Losses / MLISTRAL
We provide a model that aims to describe the impact of a massive cyber attack on an insurance portfolio, taking into account the structure of the network. Due to the contagion, such an event can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The composition of the portfolio should therefore be diversified enough to prevent or reduce the impact of such events, with the difficulty that the relationships between actor is difficult to assess. Our approach consists in introducing a multi-group epidemiological model which, apart from its ability to describe the intensity of connections between actors, can be calibrated from a relatively small amount of data, and through fast numerical procedures.