Actuarial Science & Insurance Seminar with Dr Mario Wuthrich

Abstract:

A main difficulty in actuarial claim size modeling is that there often is no simple off-the-shelf distribution that simultaneously provides a good distributional model for the main body and the tail of the data. In particular, covariates may have different effects for small and for large claim sizes. To cope with this problem, we discuss a deep composite regression model whose splicing point is given in terms of a quantile of the conditional claim size distribution rather than a constant. To facilitate M-estimation in such models, we consider and characterize the class of strictly consistent scoring functions for the triplet consisting of the

quantile, as well as the lower and upper expected shortfall beyond that quantile. In a second step, this elicitability result is applied to fit deep neural network regression models.

Biography:

Mario Wüthrich is Professor in the Department of Mathematics at ETH Zurich and Honorary Visiting Professor at Bayes Business School (2011-2022). He holds a Ph.D. in Mathematics from ETH Zurich (1999). From 2000 to 2005, he held an actuarial position at Winterthur Insurance, Switzerland. He is Actuary SAA (2004), served on the board of the Swiss Association of Actuaries (2006-2018), and is Editor-in-Chief of ASTIN Bulletin (since 2018).