In this work, we propose a methodology to predict the total cost of a natural catastrophe shortly after its
occurrence. Thanks to a large database provided through a partnership with Federation Francaise d'Assurance,
we manage to have access to a very large volume of claims (our database covers over 70% of the market). Using
meteorological data, we measure the intensity of an event. Socioeconomic data provided by INSEE (French public
statistical organization) allow to combine this information with a better knowledge of the exposure. In this work
we propose the application of dierent machine learning methods to handle this big volume of data, from sparse
Generalized Linear Models (Lasso and Elastic-Net penalties) to Random Forests.