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401-3936-DRL 1 Credits DR D-MATH

Data Analytics for Non-Life Insurance Pricing

Lecturers: Prof. Dr. Mario Valentin Wüthrich, Dr. Christoph Markus Buser
Examiners: Prof. Dr. Mario Valentin Wüthrich
Only for ZGSM (ETH D-MATH and UZH I-MATH) doctoral students. The latter need to register at myStudies and then send an email to with their name, course number and student ID. Please see
VVZ CR n/a

Last Updated: 2026-02-05 16:37:26

Abstract

We 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.

Objective

The student is familiar with classical actuarial pricing methods as well as with modern machine learning methods for insurance pricing and prediction.

Content

We 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

Resources

Lecture Notes

The lecture notes are available from:M.V. Wüthrich, C. Buser. Data Analytics for Non-Life Insurance Pricinghttp://ssrn.com/abstract=2870308

Literature

M.V. Wüthrich, M. Merz. Statistical Foundations of Actuarial Learning and its Applications http://ssrn.com/abstract=3822407

General Information

Language
English
Levels
DR
Frequency
Every two years

Examination

Type
session examination
Mode
oral 30 minutes

Registration & Places

Priority: Registration for the course unit is only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Data Analytics for Non-Life Insurance Pricing
  • Tue 16:15-18:00 (HG E 1.2)
2 h weekly

Offered In