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Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study

Author

Listed:
  • Cornwell, Nikki
  • Bilson, Christopher
  • Gepp, Adrian
  • Stern, Steven
  • Vanstone, Bruce J.

Abstract

In an effort to contribute a quantitative, objective and real-time tool to proactively and precisely manage the factors underlying and exacerbating operational risks, this pre-registered study executes the empirical methodology approved in the associated pre-registered report (Cornwell et al., 2023). The application of the Bayesian network-based approach to an Australian insurance company shows that integrating a financial institution's loss and operational data in this way can effectively model the probability of an operational loss event within its interconnected operational risk environment. Further insights and efficiencies are gained by modelling multiple operational loss events together, rather than in isolation. A novel two-module framework derived specifically for causal factors analysis from the resulting operational risk model helps to highlight the relative importance of causal factors, their collective effects and critical thresholds requiring proactivity. These insights derived from the framework are expected to be strategically valuable in helping an organisation design intentional and targeted controls for and monitoring of operational risks. Given existing knowledge of the improvements quantitative risk management tools make to risk management effectiveness and subsequently firm value, the enhanced risk management and the operational efficiencies this tool seeks to afford should ultimately contribute to driving financial performance and firm value.

Suggested Citation

  • Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered study," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:pacfin:v:79:y:2023:i:c:s0927538x2300077x
    DOI: 10.1016/j.pacfin.2023.102011
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    References listed on IDEAS

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    1. Gerry Dickinson, 2001. "Enterprise Risk Management: Its Origins and Conceptual Foundation*," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 26(3), pages 360-366, July.
    2. Terje Aven & Roger Flage, 2020. "Foundational Challenges for Advancing the Field and Discipline of Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2128-2136, November.
    3. Huang, Jinbo & Ding, Ashley & Li, Yong & Lu, Dong, 2020. "Increasing the risk management effectiveness from higher accuracy: A novel non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    4. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    5. Cornwell, Nikki & Bilson, Christopher & Gepp, Adrian & Stern, Steven & Vanstone, Bruce J., 2023. "Modernising operational risk management in financial institutions via data-driven causal factors analysis: A pre-registered report," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
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    Cited by:

    1. Hawkar Anwer Hamad & Kemal Cek, 2023. "The Moderating Effects of Corporate Social Responsibility on Corporate Financial Performance: Evidence from OECD Countries," Sustainability, MDPI, vol. 15(11), pages 1-20, May.

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    More about this item

    Keywords

    Risk management; Operational risk; Data analytics; Firm value; Financial institutions; Insurance;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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