Cyber claim analysis using Generalized Pareto regression trees with applications to insurance

With the rise of the cyber insurance market, there is a need for better quantification of the economic impact of this risk and its rapid evolution. Due to the heterogeneity of cyber claims, evaluating the appropriate premium and/or the required amount of reserves is a difficult task. In this paper, we propose a method for cyber claim analysis based on regression trees to identify criteria for claim classification and evaluation. We particularly focus on severe/extreme claims, by combining a Generalized Pareto modeling – legitimate from Extreme Value Theory – and a regression tree approach. Coupled with an evaluation of the frequency, our procedure allows computations of central scenarios and of extreme loss quantiles for a cyber portfolio. Finally, the method is illustrated on a public database.