Practical guideline to efficiently detect insurance fraud in the era of machine learning: A household insurance case

Abstract

Identifying insurance fraud is a difficult task due to the complex nature of the fraud itself, the diversity of techniques employed, the rarity of fraud cases observed in data sets, and the relatively limited allocation of human, financial, and time resources to carry out investigations. The aim of this paper is to provide a clean and well structured study on modeling fraud on home insurance contracts, using real French data from 2013 to 2017. Several methods are developed to identify risk factors and unusual customer behaviors. Traditional econometric models as well as new machine-learning algorithms with good predictive performance and high operational efficiency are tested, while maintaining method interpretability. Each methodology is evaluated on the basis of adequate performance measures and the issue of imbalanced databases is also addressed. Finally, specific methods are applied to interpret the results of the machine-learning methods.