BAUDRY Maximilien

Most statistical methods are not designed to directly work with incomplete data. The study of data incompleteness is not new and strong methods have been established to handle it prior to a statistical analysis. On the other hand, deep learning literature mainly works with unstructured data such as images, text or raw audio, but very few has been done on tabular data. Hence, modern machine learning literature tackling data incompleteness on tabular data is scarce. This thesis focuses on the use of machine learning models applied to incomplete tabular data, in an insurance context. We propose through our contributions some ways to model complex phenomena in presence of incompleteness schemes, and show that our approaches outperform the state-of-the-art models La plupart des méthodes statistiques ne sont pas nativement conçues pour fonctionner sur des données incomplètes.

Most statistical methods are not natively designed to work on incomplete data. The study of incomplete data is not new and many results have been established to overcome the incompleteness before the statistical study. On the other hand, deep learning methods are generally applied to unstructured data such as images, text or audio, but few works are interested in developing this type of approach on tabular data, and even less on incomplete data. This thesis focuses on the use of machine learning algorithms applied to tabular data, in the presence of incompleteness and in an insurance framework. Through the contributions gathered in this paper, we propose different ways to model complex phenomena in the presence of incompleteness patterns. We show that the proposed approaches give better results than the state of the art.