Operational risk modelling and organizational learning in structured finance operations: A Bayesian network approach

Andrew Darryl Sanford, Imad Ahmed Moosa

    Research output: Contribution to journalArticleResearchpeer-review

    7 Citations (Scopus)

    Abstract

    This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at- Risk, for a structured finance operations unit located within one of Australia s major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited availability of risk factor event information and operational loss data, we rely on the elicitation of subjective probabilities, sourced from domain experts. Parameter sensitivity analysis is performed to validate and check the model s robustness against the beliefs of risk management and operational staff. To ensure that the domain s evolving risk profile is captured through time, a formal approach to organizational learning is investigated that employs the automatic parameter adaption features of the Bayesian network model. A hypothetical case study is then described to demonstrate model adaption and the application of the tool to operational loss forecasting by a business unit risk manager.
    Original languageEnglish
    Pages (from-to)86 - 115
    Number of pages30
    JournalJournal of the Operational Research Society
    Volume66
    Issue number1
    DOIs
    Publication statusPublished - 2015

    Cite this

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    abstract = "This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at- Risk, for a structured finance operations unit located within one of Australia s major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited availability of risk factor event information and operational loss data, we rely on the elicitation of subjective probabilities, sourced from domain experts. Parameter sensitivity analysis is performed to validate and check the model s robustness against the beliefs of risk management and operational staff. To ensure that the domain s evolving risk profile is captured through time, a formal approach to organizational learning is investigated that employs the automatic parameter adaption features of the Bayesian network model. A hypothetical case study is then described to demonstrate model adaption and the application of the tool to operational loss forecasting by a business unit risk manager.",
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    Operational risk modelling and organizational learning in structured finance operations: A Bayesian network approach. / Sanford, Andrew Darryl; Moosa, Imad Ahmed.

    In: Journal of the Operational Research Society, Vol. 66, No. 1, 2015, p. 86 - 115.

    Research output: Contribution to journalArticleResearchpeer-review

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