Abstract
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).
| Language | English |
|---|---|
| Title of host publication | Fintech |
| Subtitle of host publication | Growth and Deregulation |
| Editors | Diane Maurice, David Fairman, Jack Freund |
| Place of Publication | United Kingdom |
| Publisher | Risk Books |
| Chapter | 3 |
| Pages | 75-99 |
| Number of pages | 25 |
| ISBN (Electronic) | 9781782723981 |
| ISBN (Print) | 9781782723875 |
| Publication status | Published - 19 Feb 2018 |
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Keywords
- Cyber Risk
- Cyber Crime
- Operational Risk
- Cyber Insurance
- Kernel K-Means
- Clustering
- Cyber Empirical Studies
Cite this
}
Statistical machine learning analysis of cyber risk data : event case studies. / Peters, Gareth W.; Shevchenko, Pavel V.; Cohen, Ruben D.; Maurice, Diane R.
Fintech: Growth and Deregulation. ed. / Diane Maurice; David Fairman; Jack Freund. United Kingdom : Risk Books, 2018. p. 75-99.Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review
TY - CHAP
T1 - Statistical machine learning analysis of cyber risk data
T2 - event case studies
AU - Peters, Gareth W.
AU - Shevchenko, Pavel V.
AU - Cohen, Ruben D.
AU - Maurice, Diane R.
PY - 2018/2/19
Y1 - 2018/2/19
N2 - This work explores the common attributes of different types ofcyber risk with a view to better understanding the key attributesthat contribute to each type of cyber risk category. In doing so weexplore event studies on a range of different market sectors,different countries, different demographics over time and categoriesof cyber risk event type.To perform this study we explore a modern machine learningclustering method to investigate the attributes of cyber riskand how they can be categorised via a statistical method. We thenexplore the properties of this statistical classification and interpretits implications for the current taxonomies being developed forcyber 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 riskbehaviour of both traditional and emerging financial institutionssuch as financial technology (fintech).
AB - This work explores the common attributes of different types ofcyber risk with a view to better understanding the key attributesthat contribute to each type of cyber risk category. In doing so weexplore event studies on a range of different market sectors,different countries, different demographics over time and categoriesof cyber risk event type.To perform this study we explore a modern machine learningclustering method to investigate the attributes of cyber riskand how they can be categorised via a statistical method. We thenexplore the properties of this statistical classification and interpretits implications for the current taxonomies being developed forcyber 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 riskbehaviour of both traditional and emerging financial institutionssuch as financial technology (fintech).
KW - Cyber Risk
KW - Cyber Crime
KW - Operational Risk
KW - Cyber Insurance
KW - Kernel K-Means
KW - Clustering
KW - Cyber Empirical Studies
UR - https://ssrn.com/abstract=3073704
M3 - Chapter
SN - 9781782723875
SP - 75
EP - 99
BT - Fintech
A2 - Maurice, Diane
A2 - Fairman, David
A2 - Freund, Jack
PB - Risk Books
CY - United Kingdom
ER -