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,...

Full description

Saved in:
Bibliographic Details
Published inInsurance, mathematics & economics Vol. 98; pp. 92 - 105
Main Authors Farkas, Sébastien, Lopez, Olivier, Thomas, Maud
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.05.2021
Elsevier Sequoia S.A
Subjects
Claims
Classification
Clustering
Cyber insurance
Economic impact
Evaluation
Extreme value analysis
Extreme value theory
Extreme values
Generalized Pareto distribution
Heterogeneity
Impact analysis
Insurance
Machine learning
Measurement
Quantiles
Regression analysis
Regression trees
Risk
Trees
Extreme value analysis
Cyber insurance
Clustering
Regression trees
Generalized Pareto distribution
Machine learning
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:0167-6687
1873-5959
DOI:10.1016/j.insmatheco.2021.02.009