Statistical machine learning analysis of cyber risk data: event case studies

Gareth W. Peters, Pavel V. Shevchenko, Ruben D. Cohen, Diane R. Maurice

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


This work explores the common attributes of different types of
cyber risk with a view to better understanding the key attributes
that contribute to each type of cyber risk category. In doing so we
explore event studies on a range of different market sectors,
different countries, different demographics over time and categories
of cyber risk event type.
To perform this study we explore a modern machine learning
clustering method to investigate the attributes of cyber risk
and how they can be categorised via a statistical method. We then
explore the properties of this statistical classification and interpret
its implications for the current taxonomies being developed for
cyber risk in areas of risk management. In the process we will interpret and analyse the implications our analysis has on both operational risk modelling of cyber risk data, as well as the implications the findings have for cyber risk insurance products. On a broader level, this analysis informs risk
behaviour of both traditional and emerging financial institutions
such as financial technology (fintech).
Original languageEnglish
Title of host publicationFintech
Subtitle of host publicationGrowth and Deregulation
EditorsDiane Maurice, David Fairman, Jack Freund
Place of PublicationUnited Kingdom
PublisherRisk Books
Number of pages25
ISBN (Electronic)9781782723981
ISBN (Print)9781782723875
Publication statusPublished - 19 Feb 2018


  • Cyber Risk
  • Cyber Crime
  • Operational Risk
  • Cyber Insurance
  • Kernel K-Means
  • Clustering
  • Cyber Empirical Studies


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