Data Analytics for Non-Life Insurance Pricing

168 Pages Posted: 17 Nov 2016 Last revised: 30 Mar 2017

Mario V. Wuthrich

RiskLab, ETH Zurich

Christoph Buser

AXA-Winterthur

Date Written: March 28, 2017

Abstract

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 machines, neural networks and support vector machines. Finally, we provide methodologies for analysing telematics car driving data from unsupervised learning.

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 (March 28, 2017). Swiss Finance Institute Research Paper No. 16-68. Available at SSRN: https://ssrn.com/abstract=2870308

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

Christoph Buser

AXA-Winterthur ( email )

Switzerland

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