Abstract

https://ssrn.com/abstract=2870308
 


 



Data Analytics for Non-Life Insurance Pricing


Mario V. Wuthrich


RiskLab, ETH Zurich; Swiss Finance Institute

Christoph Buser


AXA-Winterthur

January 27, 2017

Swiss Finance Institute Research Paper No. 16-68

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 and support vector machines. Finally, we provide methodologies for analyzing telematic car driving data.

Number of Pages in PDF File: 160

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


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Date posted: November 17, 2016 ; Last revised: January 28, 2017

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: https://ssrn.com/abstract=2870308

Contact Information

Mario V. Wuthrich (Contact Author)
RiskLab, ETH Zurich ( email )
Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland
Swiss Finance Institute

Christoph Buser
AXA-Winterthur ( email )
Switzerland
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