 CyberRisk Prediction using Machine Learning and Extreme Value TheoryJules Sadefo Kamdem and
Danielle Selambi Kapsa ()
Post-Print from HAL Abstract: This paper develops a hybrid framework for quantifying the financial impact of data breaches by combining predictive machine learning with extreme value theory (EVT). Using incident-level breach data from the Privacy Rights Clearinghouse (PRC) covering the period 2005–2020, we first estimate the number of compromised records with a Random Forest model trained on organizational, temporal, and attack-type characteristics. We then analyze the tail behavior of the predicted losses to capture the fat-tailed distribution of cyber risks. Our results indicate that the distribution of affected records is well represented by a Fr´echet law, and we estimate the parameters of the Generalized Extreme Value (GEV) distribution to compute Value-at-Risk (VaR) at high confidence levels. This two-stage approach provides a rigorous assessment of maximum potential losses, addressing the question of cyber-risk insurability. By linking predictive accuracy with tail risk quantification, our findings deliver actionable insights for insurers, regulators, and organizations seeking to anticipate and manage the f inancial consequences of large-scale data breaches. Keywords: Cyber risk; Cyber insurance; Machine learning; Random Forest; Extreme Value Theory; Value-at-Risk (search for similar items in EconPapers) Published in Information Systems Frontiers, In press There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05393158 Access Statistics for this paper More papers in Post-Print from HAL |
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