Data Analytics for Non-Life Insurance Pricing

160 Pages Posted: 17 Nov 2016 Last revised: 28 Jan 2017

Mario V. Wuthrich

RiskLab, ETH Zurich

Christoph Buser


Date Written: January 27, 2017


These notes aim at giving a broad skill set to the actuarial profession in non-life insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding. We then present several machine learning techniques such as regression trees, bagging, random forest, boosting and support vector machines. Finally, we provide methodologies for analyzing telematic car driving data.

Keywords: non-life insurance pricing, car insurance pricing, generalized linear models, generalized additive models, credibility theory, neural networks, regression trees, CART, bootstrap, bagging, random forest, boosting, support vector machines, telematic data, data science, machine learning, data analytics

JEL Classification: G22, G28

Suggested Citation

Wuthrich, Mario V. and Buser, Christoph, Data Analytics for Non-Life Insurance Pricing (January 27, 2017). Swiss Finance Institute Research Paper No. 16-68. Available at SSRN:

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092

Christoph Buser

AXA-Winterthur ( email )


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