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A Bayesian Networks Approach to Operational Risk

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

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 using only internal loss data, 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. The algorithm has been validated on synthetic time series. It should be stressed that the practical implementation of the proposed algorithm has a small impact on the organizational structure of a bank and requires an investment in human resources limited to the computational area.

Suggested Citation

  • V. Aquaro & M. Bardoscia & R. Bellotti & A. Consiglio & F. De Carlo & G. Ferri, 2009. "A Bayesian Networks Approach to Operational Risk," Papers 0906.3968, arXiv.org, revised Feb 2012.
    • Handle: RePEc:arx:papers:0906.3968
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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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    1. 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.
    2. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    3. 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.
    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. 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.
    6. 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.
    7. 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.
    8. 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).

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