Insights from Inside Neural Networks

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, as well as over-fitting. This discussion is based on a publicly available real car insurance data set.