L’émergence du boosting dans le domaine du machine learning a rapidement gagné en popularité parmi les actuaires. Les distributions de Tweedie (dont la Poisson, la Gamma) et binomiale sont les plus couramment utilisées en assurance par exemple pour les modèles de tarification.
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This working note starts with a review of the k-means algorithm and develops next two extensions to manage categorical features.
The expansion of the cyber insurance market is constantly under the threat of an accumulation event that would simultaneously affect a large number of policyholders…
This article introduces an equity-Libor Market Model (LMM) that integrates the investment strategy into the valuation process of participating life insurances.
This article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs)…
Generalized additive models (GAMs) are a leading model class for interpretable machine learning. GAMs were originally trained using smoothing splines.
This paper proposes a variant of the well-known boosting trees algorithm to estimate conditional distributions.
In the present note, we present a general methodology to process text data using neural networks, and how it can be used to determine the severity of a cyber incident when this information is missing.
Hainaut et al. (2022) established that boosting can be conducted directly on the response under Tweedie loss function and log-link, by adapting the weights at each step. This is particularly useful to analyze low counts…
This article proposes a tail index partition-based rules extraction method that is able to construct estimates of the partition subsets and estimates of the tail index values.
This article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs)…
