Insights from Inside Neural Networks

64 Pages Posted: 19 Aug 2018 Last revised: 24 Apr 2020

See all articles by Andrea Ferrario

Andrea Ferrario

Dep. Management, Technology, and Economics ETH Zurich; Mobiliar Lab for Analytics at ETH

Alexander Noll

PartnerRe Ltd - PartnerRe Holdings Europe Limited

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: April 23, 2020

Abstract

We provide a tutorial that illuminates the aspects which need to be considered when fitting neural network regression models to claims frequency data in insurance. We discuss feature pre-processing, choice of loss function, choice of neural network architecture, class imbalance problem, balance property and bias regularization as well as over-fitting. This discussion is based on a publicly available real car insurance data set.

Keywords: Neural Networks, Architecture, Over-Fitting, Loss Function, Dropout, Regularization, LASSO, Ridge, Gradient Descent, Class Imbalance, Car Insurance, Claims Frequency, Poisson Regression Model, Machine Learning, Deep Learning

JEL Classification: G22, C10, C13, C14, C67

Suggested Citation

Ferrario, Andrea and Noll, Alexander and Wuthrich, Mario V., Insights from Inside Neural Networks (April 23, 2020). Available at SSRN: https://ssrn.com/abstract=3226852 or http://dx.doi.org/10.2139/ssrn.3226852

Andrea Ferrario

Dep. Management, Technology, and Economics ETH Zurich ( email )

Zurich
Switzerland

Mobiliar Lab for Analytics at ETH ( email )

Zürich, 8092
Switzerland

Alexander Noll

PartnerRe Ltd - PartnerRe Holdings Europe Limited ( email )

160 Shelbourne Road
Dublin, 4
Ireland

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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