Experience Rating in Insurance Pricing

162 Pages Posted: 12 Mar 2024

Date Written: February 14, 2024


These notes are part of the syllabus of statistical modeling in non-life insurance. Subsequent to the introductory classes on 'Non-Life Insurance: Mathematics & Statistics' and 'Data Analytics for Non-Life Insurance Pricing', these notes extend the fixed effects models to mixed effects models, also integrating past claims history for insurance pricing. We study different approaches (a) fixed effects models not considering any past claims experience, (b) random effects models only considering past claims history, (c) mixed effects models combining fixed effects and random effects considerations. This can be achieved by either using static random effects models or dynamic random effects models. Generally, static random effects models are more easy to handle, however, they do not allow for a seniority weighting of past claims, in contrast to their dynamic counterparts. These models are complemented by machine learning approaches for experience rating, which mainly rely on network architectures that include an attention weight mechanism. All considered models are supported by data examples.

Keywords: Experience rating, posterior rating, bonus-malus systems, generalized linear models, neural networks, attention layer, Transformer, fixed effects models, random effects models, mixed effects models, panel data, cross-sectional data, credibility theory, Bayesian theory

JEL Classification: G22

Suggested Citation

Wuthrich, Mario V., Experience Rating in Insurance Pricing (February 14, 2024). Available at SSRN: https://ssrn.com/abstract=4726206 or http://dx.doi.org/10.2139/ssrn.4726206

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics