Bias Regularization in Neural Network Models for General Insurance Pricing

23 Pages Posted: 28 Mar 2019

Date Written: March 5, 2019

Abstract

Generalized linear models have the important property of providing unbiased estimates on a portfolio level. This implies that generalized linear models manage to provide accurate prices on a portfolio level. On the other hand, neural networks may provide very accurate prices on an individual policy level, but state-of-the-art use of neural networks does not pay attention to unbiasedness on a portfolio level. In fact, this is an implicit consequence of using early stopping rules in gradient descent methods for model fitting. In the present paper we discuss this deficiency and we provide two different techniques that remove this drawback of neural network model fitting.

Keywords: generalized linear model; exponential dispersion family; neural network; gradient descent method; unbiasedness; regression tree

JEL Classification: G22; C13

Suggested Citation

Wuthrich, Mario V., Bias Regularization in Neural Network Models for General Insurance Pricing (March 5, 2019). Available at SSRN: https://ssrn.com/abstract=3347177 or http://dx.doi.org/10.2139/ssrn.3347177

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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