Case Study: French Motor Third-Party Liability Claims

39 Pages Posted: 18 Apr 2018 Last revised: 14 Nov 2018

See all articles by Alexander Noll

Alexander Noll

PartnerRe Ltd - PartnerRe Holdings Europe Limited

Robert Salzmann

SIGNAL IDUNA Reinsurance Ltd

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: November 8, 2018

Abstract

We provide a tutorial that compares a classical generalized linear model for claims frequency modeling to regression tree, boosting machine and neural network approaches. We explore these methods, discuss their calibration and study their predictive power on an explicit motor third-party liability insurance data set. The results of the case study show that a simple generalized linear model does not capture interactions of feature components appropriately, whereas the other methods are able to address these interactions.

Keywords: data science, machine learning, predictive modeling, claims frequency, motor insurance, regression trees, boosting machine, neural network, generalized linear models, feature engineering, covariate selection

JEL Classification: G22

Suggested Citation

Noll, Alexander and Salzmann, Robert and Wuthrich, Mario V., Case Study: French Motor Third-Party Liability Claims (November 8, 2018). Available at SSRN: https://ssrn.com/abstract=3164764 or http://dx.doi.org/10.2139/ssrn.3164764

Alexander Noll

PartnerRe Ltd - PartnerRe Holdings Europe Limited ( email )

160 Shelbourne Road
Dublin, 4
Ireland

Robert Salzmann

SIGNAL IDUNA Reinsurance Ltd ( email )

Bundesplatz 1
Postfach 7737
Zug, Zug 6302
Switzerland

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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