Ruben D. Cohen, Jonathan Humphries, S. Veau, R. Francis
{"title":"An Investigation of Cyber Loss Data and Its Links to Operational Risk","authors":"Ruben D. Cohen, Jonathan Humphries, S. Veau, R. Francis","doi":"10.21314/jop.2019.228","DOIUrl":null,"url":null,"abstract":"Cyber risk is one of the most challenging areas of risk, not only because it is relatively nascent but also because it remains an elusive moving target due to an ever-evolving threat landscape. A lack of structured data and the systemic implications of multifaceted impacts of overlapping risk frameworks are additional factors that make this risk difficult to quantify. As a starting point for overcoming this challenge, our paper considers a potential definition of this risk type, encompassing confidentiality, integrity and availability; the key components of a cyber-risk framework; a taxonomy to help establish a common framework for data collection to aid quantification; and the key quantification challenges. It then focuses on quantifying the direct financial and compensatory losses emanating from cyber risks. To help us carry this out, dimensional analysis is incorporated in the same manner as it has been applied to operational losses; this enables the identification of any similarities and/ or gross deviations between the profiles of cyber and non-cyber operational losses. In all, considering the limited amount of cyber data available, this analysis shows that: \n \n(1) a taxonomy for cyber risk that maps directly to operational risk might be a worthwhile exercise; \n \n(2) cyber loss data has a fundamental risk profile similar to that of non-cyber operational risk losses, with both following the same trend; and \n \n(3) the underlying risk profile related to cyber losses has not changed materially over time. \n \nThese findings come with the added implications that: \n \n(1) mapping the taxonomies of cyber and operational risk against each other could be conducted more objectively; \n \n(2) operational risk modeling techniques that have been developed over the past decade or so could be used in the same way to assess the direct financial impact of cyber risk as a starting point; and \n \n(3) although there has been an increase in both the frequency and the severity of cyber losses over the past few years, there has not been a major paradigm shift in their fundamental risk profile over the same period of time.","PeriodicalId":54030,"journal":{"name":"Journal of Operational Risk","volume":"59 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2019-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Operational Risk","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.21314/jop.2019.228","RegionNum":4,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 12
Abstract
Cyber risk is one of the most challenging areas of risk, not only because it is relatively nascent but also because it remains an elusive moving target due to an ever-evolving threat landscape. A lack of structured data and the systemic implications of multifaceted impacts of overlapping risk frameworks are additional factors that make this risk difficult to quantify. As a starting point for overcoming this challenge, our paper considers a potential definition of this risk type, encompassing confidentiality, integrity and availability; the key components of a cyber-risk framework; a taxonomy to help establish a common framework for data collection to aid quantification; and the key quantification challenges. It then focuses on quantifying the direct financial and compensatory losses emanating from cyber risks. To help us carry this out, dimensional analysis is incorporated in the same manner as it has been applied to operational losses; this enables the identification of any similarities and/ or gross deviations between the profiles of cyber and non-cyber operational losses. In all, considering the limited amount of cyber data available, this analysis shows that:
(1) a taxonomy for cyber risk that maps directly to operational risk might be a worthwhile exercise;
(2) cyber loss data has a fundamental risk profile similar to that of non-cyber operational risk losses, with both following the same trend; and
(3) the underlying risk profile related to cyber losses has not changed materially over time.
These findings come with the added implications that:
(1) mapping the taxonomies of cyber and operational risk against each other could be conducted more objectively;
(2) operational risk modeling techniques that have been developed over the past decade or so could be used in the same way to assess the direct financial impact of cyber risk as a starting point; and
(3) although there has been an increase in both the frequency and the severity of cyber losses over the past few years, there has not been a major paradigm shift in their fundamental risk profile over the same period of time.
期刊介绍:
In December 2017, the Basel Committee published the final version of its standardized measurement approach (SMA) methodology, which will replace the approaches set out in Basel II (ie, the simpler standardized approaches and advanced measurement approach (AMA) that allowed use of internal models) from January 1, 2022. Independently of the Basel III rules, in order to manage and mitigate risks, they still need to be measurable by anyone. The operational risk industry needs to keep that in mind. While the purpose of the now defunct AMA was to find out the level of regulatory capital to protect a firm against operational risks, we still can – and should – use models to estimate operational risk economic capital. Without these, the task of managing and mitigating capital would be incredibly difficult. These internal models are now unshackled from regulatory requirements and can be optimized for managing the daily risks to which financial institutions are exposed. In addition, operational risk models can and should be used for stress tests and Comprehensive Capital Analysis and Review (CCAR). The Journal of Operational Risk also welcomes papers on nonfinancial risks as well as topics including, but not limited to, the following. The modeling and management of operational risk. Recent advances in techniques used to model operational risk, eg, copulas, correlation, aggregate loss distributions, Bayesian methods and extreme value theory. The pricing and hedging of operational risk and/or any risk transfer techniques. Data modeling external loss data, business control factors and scenario analysis. Models used to aggregate different types of data. Causal models that link key risk indicators and macroeconomic factors to operational losses. Regulatory issues, such as Basel II or any other local regulatory issue. Enterprise risk management. Cyber risk. Big data.