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401-3938-00L 4 Credits BSC , DR , MSC D-MATH

Experience Rating in Insurance Pricing

Lecturers & Examiners: Prof. Dr. Mario Valentin Wüthrich
VVZ CR n/a

Last Updated: 2026-06-01 11:33:47

Abstract

This lecture presents several methods for general insurance pricing. It starts from prior rating information discussing generalized linear models and neural networks. These models are extended to a dynamic view by using past claims history. It considers static and dynamic insurance pricing models, including mixed effects models, Bühlmann credibility, state-space models and transformers.

Objective

The student is familiar with advanced actuarial pricing methods in general insurance as well as with modern machine learning methods for insurance pricing and prediction. The student is able to use statistical and machine learning methods in an actuarial context.

Content

We present the following topics: - generalized linear models (GLMs) - neural networks - the balance property and auto-calibration - Bühlmann credibility theory - empirical Bayes methods - dynamic mixed effects models - observation-driven state-space models - deep experience rating - transformers and attention layers

Resources

Lecture Notes

The lecture notes are available from:M.V. Wüthrich. Experience Rating in Insurance Pricinghttp://ssrn.com/abstract=4726206Data and code is available from:https://github.com/wueth/Experience-Rating-in-Insurance-Pricing

Literature

Further literature: M.V. Wüthrich, M. Merz. Statistical Foundations of Actuarial Learning and its Applications, Springer 2023. https://link.springer.com/book/10.1007/978-3-031-12409-9

General Information

Language
English
Levels
BSC , DR , MSC
Frequency
Every two years

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 Experience Rating in Insurance Pricing
  • Tue 16:15-18:00 (HG D 5.2)
2 h weekly

Offered In