Statistical Machine Learning Analysis of Cyber Risk Data: Event Case Studies

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

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-based 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).


The aim of this work is to study the properties of cyber risk from the

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