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Modeling Operational Risk With Bayesian Networks

Author

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  • R. G. Cowell
  • R. J. Verrall
  • Y. K. Yoon

Abstract

Bayesian networks is an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of financial institutions have resulted in increased scrutiny on the risk management of banks and insurance companies, thus giving the industry an impetus to measure and manage operational risk. The more established methods for risk quantification are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various nonlinear methods have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, we construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real‐time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested.

Suggested Citation

  • R. G. Cowell & R. J. Verrall & Y. K. Yoon, 2007. "Modeling Operational Risk With Bayesian Networks," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(4), pages 795-827, December.
  • Handle: RePEc:bla:jrinsu:v:74:y:2007:i:4:p:795-827
    DOI: 10.1111/j.1539-6975.2007.00235.x
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    Cited by:

    1. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    2. Marco Bardoscia & Roberto Bellotti, 2012. "A Dynamical Approach to Operational Risk Measurement," Papers 1202.2532, arXiv.org.
    3. Häger, David & Andersen, Lasse B., 2010. "A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1635-1644, December.
    4. Rajendra P. Srivastava & Theodore J. Mock & Jerry L. Turner, 2009. "Bayesian Fraud Risk Formula for Financial Statement Audits," Abacus, Accounting Foundation, University of Sydney, vol. 45(1), pages 66-87, March.
    5. Zangeneh, Pouya & McCabe, Brenda, 2022. "Modelling socio-technical risks of industrial megaprojects using Bayesian Networks and reference classes," Resources Policy, Elsevier, vol. 79(C).
    6. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    7. William Percy & Kevin Dow, 2021. "The Coaching Black Box: Risk Mitigation during Change Management," JRFM, MDPI, vol. 14(8), pages 1-18, July.
    8. Marcelo Ramos Martins & Adriana Miralles Schleder & Enrique López Droguett, 2014. "A Methodology for Risk Analysis Based on Hybrid Bayesian Networks: Application to the Regasification System of Liquefied Natural Gas Onboard a Floating Storage and Regasification Unit," Risk Analysis, John Wiley & Sons, vol. 34(12), pages 2098-2120, December.
    9. Bertrand K. Hassani & Alexis Renaudin, 2018. "The Cascade Bayesian Approach: Prior Transformation for a Controlled Integration of Internal Data, External Data and Scenarios," Risks, MDPI, vol. 6(2), pages 1-17, April.
    10. Emma Apps, 2020. "Applying a Bayesian Network to VaR Calculations," Working Papers 202024, University of Liverpool, Department of Economics.
    11. Yash Daultani & Mohit Goswami & Omkarprasad S. Vaidya & Sushil Kumar, 2019. "Inclusive risk modeling for manufacturing firms: a Bayesian network approach," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2789-2803, December.

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