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401-3936-00L 4 Credits BSC , DR , MSC D-ITET , D-INFK , D-MATH
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Data Analytics for Non-Life Insurance Pricing

Lecturers: Prof. Dr. Mario Valentin Wüthrich, Dr. Christoph Markus Buser
Examiners: Prof. Dr. Mario Valentin Wüthrich
VVZ CR n/a

Last Updated: 2026-02-05 16:22:58

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
BSC , DR , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 30 minutes
only in person exams (i.e. no remote exams)

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