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A Bayesian Networks approach to Operational Risk

Author

Listed:
  • Aquaro, V.
  • Bardoscia, M.
  • Bellotti, R.
  • Consiglio, A.
  • De Carlo, F.
  • Ferri, G.

Abstract

A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.

Suggested Citation

  • Aquaro, V. & Bardoscia, M. & Bellotti, R. & Consiglio, A. & De Carlo, F. & Ferri, G., 2010. "A Bayesian Networks approach to Operational Risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1721-1728.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:8:p:1721-1728
    DOI: 10.1016/j.physa.2009.12.043
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    References listed on IDEAS

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    1. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169.
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    2. Huang, Zhaodong & Chien, Steven & Zhu, Wei & Zheng, Pengjun, 2022. "Scheduling wheel inspection for sustainable urban rail transit operation: A Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    3. Francisco Venegas-Martínez & José Francisco Martínez-Sánchez & María Teresa V. Martínez-Palacios, 2016. "An analysis on operational risk in international banking: A Bayesian approach (2007–2011)," Estudios Gerenciales, Universidad Icesi, vol. 32(140), pages 208-220, September.
    4. Jia, Xiaoliang & An, Haizhong & Sun, Xiaoqi & Huang, Xuan & Gao, Xiangyun, 2016. "Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 331-344.
    5. Baoping Cai & Yonghong Liu & Zengkai Liu & Xiaojie Tian & Yanzhen Zhang & Renjie Ji, 2013. "Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations," Risk Analysis, John Wiley & Sons, vol. 33(7), pages 1293-1311, July.
    6. Bonnie C. Wintle & Ann Nicholson, 2014. "Exploring Risk Judgments in a Trade Dispute Using Bayesian Networks," Risk Analysis, John Wiley & Sons, vol. 34(6), pages 1095-1111, June.
    7. Wang, Zongrun & Wang, Wuchao & Chen, Xiaohong & Jin, Yanbo & Zhou, Yanju, 2012. "Using BS-PSD-LDA approach to measure operational risk of Chinese commercial banks," Economic Modelling, Elsevier, vol. 29(6), pages 2095-2103.
    8. E. Cene & F. Karaman, 2015. "Analysing organic food buyers' perceptions with Bayesian networks: a case study in Turkey," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(7), pages 1572-1590, July.

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