Modelling Fire Risk Exposure for France Using Machine Learning

Abstract

Wildfires generating damage to assets are extremely rare in France. The peril is not covered by the French natural catastrophes insurance scheme (law of 13 July 1982). In the context of the changing climate, Caisse Centrale de Réassurance—the French state-owned reinsurance company involved in the Nat Cat insurance scheme—decided to develop its knowledge on the national exposure of France to wildfire risks. Current and future forest fires events have to be anticipated in case one of the events threatens buildings. The present work introduces the development of a catastrophe loss risk model (Cat model) for forest fires for the French metropolitan area. Cat models are the tools used by the (re)insurance sector to assess their portfolios’ exposure to natural disasters. The open-source national Promethée database focusing on the South of France for the period 1973–2019 was used as training data for the development of the hazard unit using machine learning-based methods. As a result, we observed an extension of the exposure to wildfire in northern areas, namely Landes, Pays-de-la-Loire, and Bretagne, under the RCP 4.5 scenario. The work highlighted the need to understand the multi-peril exposure of the French country and the related economic damage. This is the first study of this kind performed by a reinsurance company in collaboration with a scholarly institute, in this case EURIA Brest.