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A knowledge based approach to loss severity assessment in financial institutions using Bayesian networks and loss determinants

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  • Häger, David
  • Andersen, Lasse B.

Abstract

Modelling loss severity from rare operational risk events with potentially catastrophic consequences has proved a difficult task for practitioners in the finance industry. Efforts to develop loss severity models that comply with the BASEL II Capital Accord have resulted in two principal model directions where one is based on scenario generated data and the other on scaling of pooled external data. However, lack of relevant historical data and difficulties in constructing relevant scenarios frequently raise questions regarding the credibility of the resulting loss predictions. In this paper we suggest a knowledge based approach for establishing severity distributions based on loss determinants and their causal influence. Loss determinants are key elements affecting the actual size of potential losses, e.g. market volatility, exposure and equity capital. The loss severity distribution is conditional on the state of the identified loss determinants, thus linking loss severity to underlying causal drivers. We suggest Bayesian Networks as a powerful framework for quantitative analysis of the causal mechanisms determining loss severity. Leaning on available data and expert knowledge, the approach presented in this paper provides improved credibility of the loss predictions without being dependent on extensive data volumes.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:207:y:2010:i:3:p:1635-1644
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    References listed on IDEAS

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    1. Milan Rippel & Petr Teplý, 2011. "Operational Risk - Scenario Analysis," Prague Economic Papers, University of Economics, Prague, vol. 2011(1), pages 23-39.
    2. repec:eee:reensy:v:94:y:2009:i:10:p:1499-1509 is not listed on IDEAS
    3. Dahen, Hela & Dionne, Georges, 2010. "Scaling models for the severity and frequency of external operational loss data," Journal of Banking & Finance, Elsevier, vol. 34(7), pages 1484-1496, July.
    4. 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.
    5. Kleinmuntz, Don N. & Fennema, M. G. & Peecher, Mark E., 1996. "Conditioned Assessment of Subjective Probabilities: Identifying the Benefits of Decomposition," Organizational Behavior and Human Decision Processes, Elsevier, vol. 66(1), pages 1-15, April.
    6. Brown, Stephen J. & Steenbeek, Onno W., 2001. "Doubling: Nick Leeson's trading strategy," Pacific-Basin Finance Journal, Elsevier, vol. 9(2), pages 83-99, April.
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    Cited by:

    1. Feria-Domínguez, José Manuel & Jiménez-Rodríguez, Enrique & Sholarin, Ola, 2015. "Tackling the over-dispersion of operational risk: Implications on capital adequacy requirements," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 206-221.
    2. Ülengin, Füsun & Önsel, Şule & Aktas, Emel & Kabak, Özgür & Özaydın, Özay, 2014. "A decision support methodology to enhance the competitiveness of the Turkish automotive industry," European Journal of Operational Research, Elsevier, vol. 234(3), pages 789-801.
    3. Kabir, Golam & Tesfamariam, Solomon & Francisque, Alex & Sadiq, Rehan, 2015. "Evaluating risk of water mains failure using a Bayesian belief network model," European Journal of Operational Research, Elsevier, vol. 240(1), pages 220-234.

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