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

DOI10.1016/j.insmatheco.2021.02.009zbMath1466.91255OpenAlexW3137381002MaRDI QIDQ2034155

Olivier Lopez, Sébastien Farkas, Maud Thomas

Publication date: 21 June 2021

Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2021.02.009

zbMATH Keywords

extreme value analysisclusteringmachine learningregression treesgeneralized Pareto distributioncyber insurance

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Extreme value theory; extremal stochastic processes (60G70) Actuarial mathematics (91G05)

Related Items (8)

Cyber risk frequency, severity and insurance viability ⋮ Exact Insurance Premiums for Cyber Risk of Small and Medium-Sized Enterprises ⋮ The Economic Impact of Extreme Cyber Risk Scenarios ⋮ Building up cyber resilience by better grasping cyber risk via a new algorithm for modelling heavy-tailed data ⋮ Multivariate claim processes with rough intensities: properties and estimation ⋮ Gradient boosting for extreme quantile regression ⋮ Tail index partition-based rules extraction with application to tornado damage insurance ⋮ An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests

Uses Software

• rpart
• ismev

Cites Work

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• Statistics of Extremes
• Fifty Years of Classification and Regression Trees
• Testing Statistical Hypotheses
• Heavy-Tail Phenomena
• An introduction to statistical modeling of extreme values

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