401-3936-DRL  Data Analytics for Non-Life Insurance Pricing

SemesterFrühjahrssemester 2023
DozierendeM. V. Wüthrich, C. M. Buser
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarOnly for ETH D-MATH doctoral students and for doctoral students from the Institute of Mathematics at UZH. The latter need to send an email to Jessica Bolsinger (info@zgsm.ch) with the course number. The email should have the subject „Graduate course registration (ETH)“.


KurzbeschreibungWe study statistical methods in supervised learning for non-life insurance pricing such as generalized linear models, generalized additive models, Bayesian models, neural networks, classification and regression trees, random forests and gradient boosting machines.
LernzielThe student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction.
InhaltWe present the following chapters:
- generalized linear models (GLMs)
- generalized additive models (GAMs)
- neural networks
- credibility theory
- classification and regression trees (CARTs)
- bagging, random forests and boosting
SkriptThe lecture notes are available from:
M.V. Wüthrich, C. Buser. Data Analytics for Non-Life Insurance Pricing
http://ssrn.com/abstract=2870308
LiteraturM.V. Wüthrich, M. Merz. Statistical Foundations of Actuarial Learning and its Applications
http://ssrn.com/abstract=3822407
Voraussetzungen / BesonderesThis course will be held in English and counts towards the diploma of "Aktuar SAV".
For the latter, see details under www.actuaries.ch

Good knowledge in probability theory, stochastic processes and statistics is assumed.