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Using Bayesian Networks to Model Expected and Unexpected Operational Losses

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  • Martin Neil
  • Norman Fenton
  • Manesh Tailor

Abstract

This report describes the use of Bayesian networks (BNs) to model statistical loss distributions in financial operational risk scenarios. Its focus is on modeling “long” tail, or unexpected, loss events using mixtures of appropriate loss frequency and severity distributions where these mixtures are conditioned on causal variables that model the capability or effectiveness of the underlying controls process. The use of causal modeling is discussed from the perspective of exploiting local expertise about process reliability and formally connecting this knowledge to actual or hypothetical statistical phenomena resulting from the process. This brings the benefit of supplementing sparse data with expert judgment and transforming qualitative knowledge about the process into quantitative predictions. We conclude that BNs can help combine qualitative data from experts and quantitative data from historical loss databases in a principled way and as such they go some way in meeting the requirements of the draft Basel II Accord (Basel, 2004) for an advanced measurement approach (AMA).

Suggested Citation

  • Martin Neil & Norman Fenton & Manesh Tailor, 2005. "Using Bayesian Networks to Model Expected and Unexpected Operational Losses," Risk Analysis, John Wiley & Sons, vol. 25(4), pages 963-972, August.
  • Handle: RePEc:wly:riskan:v:25:y:2005:i:4:p:963-972
    DOI: 10.1111/j.1539-6924.2005.00641.x
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    Cited by:

    1. Yuqian Xu & Lingjiong Zhu & Michael Pinedo, 2020. "Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls," Operations Research, INFORMS, vol. 68(6), pages 1804-1825, November.
    2. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    3. C. L. Smith & E. Borgonovo, 2007. "Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 1027-1042, August.
    4. Yuqian Xu & Tom Fangyun Tan & Serguei Netessine, 2022. "The Impact of Workload on Operational Risk: Evidence from a Commercial Bank," Management Science, INFORMS, vol. 68(4), pages 2668-2693, April.
    5. Yuan Hong & Shaojian Qu, 2024. "Beyond Boundaries: The AHP-DEA Model for Holistic Cross-Banking Operational Risk Assessment," Mathematics, MDPI, vol. 12(7), pages 1-18, March.
    6. Ballester, Laura & López, Jesúa & Pavía, Jose M., 2023. "European systemic credit risk transmission using Bayesian networks," Research in International Business and Finance, Elsevier, vol. 65(C).
    7. Xu, Chi & Zheng, Chunling & Wang, Donghua & Ji, Jingru & Wang, Nuan, 2019. "Double correlation model for operational risk: Evidence from Chinese commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 327-339.
    8. Emma Apps, 2020. "Applying a Bayesian Network to VaR Calculations," Working Papers 202024, University of Liverpool, Department of Economics.
    9. Johnson Holt & Adrian W. Leach & Gritta Schrader & Françoise Petter & Alan MacLeod & Dirk Jan van der Gaag & Richard H. A. Baker & John D. Mumford, 2014. "Eliciting and Combining Decision Criteria Using a Limited Palette of Utility Functions and Uncertainty Distributions: Illustrated by Application to Pest Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 4-16, January.
    10. Hatoum, Khalil & Moussu, Christophe & Gillet, Roland, 2022. "CEO overconfidence: Towards a new measure," International Review of Financial Analysis, Elsevier, vol. 84(C).

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