Mario V. Wüthrich - Statistical Foundations of Actuarial Learning and its Applications (2022) [Wüthrich Merz] [9783031124099]

This is a series on actuarial topics in a broad and interdisciplinary sense, aimed at students, academics and practitioners in the fields of insurance and finance.

Springer Actuarial informs timely on theoretical and practical aspects of topics like risk management, internal models, solvency, asset-liability management, market-consistent valuation, the actuarial control cycle, insurance and financial mathematics, and other related interdisciplinary areas.

The series aims to serve as a primary scientific reference for education, research, development and model validation.

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This work was supported by Schweizerische Aktuarvereinigung SAV and Swiss Re.

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We kindly thank our very generous sponsors, the Swiss Association of Actuaries (SAA) and Swiss Re, for financing the open access option of the electronic version of this book. Our special thanks go to Sabine Betz (President of SAA), Adrian Kolly (Swiss Re), and Holger Walz (SAA) who were very positive and interested in this book project from the very beginning, and who made this open access funding possible within their institutions.

A very special thank you goes to Hans Bhlmann who has been supporting us over the last 30 years. We have had so many inspiring discussions over these years, and we have greatly benefited and learned from Hans incredible knowledge and intuition.

Jointly with Christoph Buser, we have started to teach the lecture Data Analytics for Non-Life Insurance Pricing at ETH Zurich in 2018. Our data analytics lecture focuses (only) on the Poisson claim counts case, but its lecture notes have provided a first draft for this book project. This draft has been developed and extended to the general case of the exponential family. Since our first lecture, we have greatly benefited from interactions with many colleagues and students. In particular, we would like to mention the data science initiative Actuarial Data Science of the Swiss Association of Actuaries (chaired by Jrg Schelldorfer), whose tutorials provided a great stimulus for this book. Moreover, we mention the annual Insurance Data Science Conference (chaired by Markus Gesmann and Andreas Tsanakas) and the ASTIN Reading Club (chaired by Ronald Richman and Dimitri Semenovich). Furthermore, we would like to kindly thank Ronald Richman who has always been a driving force behind learning and adapting new machine learning techniques, and we also kindly thank Simon Rentzmann for many interesting discussions on how to apply these techniques on real insurance problems.

We thank the following colleagues by name (in alphabetical order). We collaborated and had inspiring discussions in the field of statistical learning with the following colleagues: Johannes Abegglen, Hansjrg Albrecher, Davide Apolloni, Peter Bhlmann, Christoph Buser, Patrick Cheridito, ukasz Delong, Paul Embrechts, Andrea Ferrario, Tobias Fissler, Luca Fontana, Daisuke Frei, Tsz Chai Fung, Guangyuan Gao, Yan-Xing Lan, Gee Lee, Mathias Lindholm, Christian Lorentzen, Friedrich Loser, Michael Mayer, Daniel Meier, Alexander Noll, Gareth Peters, Jan Rabenseifner, Peter Reinhard, Simon Rentzmann, Ronald Richman, Ludger Rschendorf, Robert Salzmann, Marc Sarbach, Jrg Schelldorfer, Pavel Shevchenko, Jol Thomann, Andreas Tsanakas, George Tzougas, Emiliano Valdez, Tim Verdonck, and Patrick Zchbauer.