Individual Claims Reserving in Creditor Protection Insurance Using Machine Learning

Abstract

In the context of individual claims reserving in non-life insurance, a new perspective involving machine learning techniques was recently introduced. We focused on credit insurance which, despite being seldom explored, can represent an interesting challenge for machine learning techniques because of its volatile nature, sensitive to economic trends. In a framework where insurance undertakings are collecting an increasing amount of data, methods like Neural Networks and Support Vector Machines could provide a valid alternative to traditional reserving techniques, offering an easy way to include macro-economic information in the estimation process. While recent machine learning literature have focused mainly on case reserving and on analysis of loss development triangles, in this work we provide a complete evaluation of each component of the Claims Reserve in a granular sense and we compare, in terms of both bias and variability, their results with Generalised Linear Models, which can be considered a standard actuarial tool.