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The use of unstructured data in (re)insurance has become a central topic with the arrival of new statistical practices related to data science. Today, we have many examples that prove there is a growing interest in gathering and exploiting these data: opt
The aim of this paper is to introduce a synthetic ALM model that catches the main specificity of life insurance contracts. First, it keeps track of both market and book values to apply the regulatory profit-sharing rule. Second, it introduces a determinat
Tree-based methods are convenient and powerful machine learning tools thatcan be seen as alternatives to classical regression and prediction models suchas generalized linear models, see for example. The most standard proceduresare designed to estimate the
In this paper, we develop stochastic models to determine the impact of a massive cyber attack on an insurance portfolio. The model is based on the classical SIR framework (Susceptible - Infected - Recovered) of epidemiological models. For a given type of
In this paper we propose an actuarial framework and a statistical methodology allowing the quantification of Cyber claims resulting from data breaches events even when applied on few and heterogeneous data. Indeed, for now, just a few Cyber insurance clai
Abstract Unstructured data such as text remain quite untapped nowadays in the (re)insurance industry. The first basic reason of this probably comes from the unawareness of the way to handle well these texts. Natural language processing (NLP) held propose