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Using Bayesian networks to model the operational risk to information technology infrastructure in financial institutions

Author

Listed:
  • Neil, Martin

    () (Queen Mary University of London)

  • Fenton, Norman

    () (Queen Mary University of London)

Abstract

This paper describes the use of Bayesian networks (BNs) to model the operational risk to information technology (IT) infrastructure in financial and other institutions. We describe a methodology for modeling financial losses that might result from operational risk scenarios involving data centers and operational locations, applications and systems, processes, and ultimately IT supported customer-facing services. We focus on modeling the causes and effects of unexpected loss events using a Bayesian network model of the IT infrastructure combined with assessments of the severity of impact of these events in terms of the Value at Risk (VaR) for the organization. We use a state-of-the-art Bayesian network tool to simulate an example analysis of the model. The work also illustrates how ideas commonly used to measure risk in other industries, especially the Aviation and Nuclear sectors, readily translate to operational risk in finance.

Suggested Citation

  • Neil, Martin & Fenton, Norman, 2008. "Using Bayesian networks to model the operational risk to information technology infrastructure in financial institutions," Journal of Financial Transformation, Capco Institute, vol. 22, pages 131-138.
  • Handle: RePEc:ris:jofitr:0929
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    References listed on IDEAS

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    Cited by:

    1. Semir Ibrahimovic & Nijaz Bajgoric, 2016. "Modeling information system availability by using bayesian belief network approach," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 14(2), pages 125-138.

    More about this item

    Keywords

    Bayesian networks; operational risk;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    Statistics

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