Extending generalized linear models in insurance with machine learning techniques

Abstract

Machine learning models have shown promising results regarding their predictive power. However, little to no information about their use of variables is available. The aim of this thesis is to introduce and put into practice two ways of extracting this insight about variable use. This insight is applied to produce interpretable models that predict in a similar way to underlying machine learning models. The first three chapters give a theoretical overview of methods used to build models and extract insight, and the last two chapters focus on applying these methods to predict claim frequency using real-life insurance data.