Data Analytics for Non-Life Insurance Pricing

241 Pages Posted: 17 Nov 2016 Last revised: 5 Jun 2019

Date Written: June 4, 2019

Abstract

These notes aim at giving a broad skill set to the actuarial profession in 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 and neural networks. 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, 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 (June 4, 2019). Swiss Finance Institute Research Paper No. 16-68. Available at SSRN: https://ssrn.com/abstract=2870308 or http://dx.doi.org/10.2139/ssrn.2870308

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

Christoph Buser

AXA-Winterthur ( email )

Switzerland

Register to save articles to
your library

Register

Paper statistics

Downloads
4,428
Abstract Views
8,936
rank
1,829
PlumX Metrics