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Operational and cyber risks in the financial sector

Author

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  • Gambacorta, Leonardo
  • Aldasoro, Inaki
  • Giudici, Paolo
  • Leach, Thomas

Abstract

We use a unique cross-country dataset at the loss event level to document the evolution and characteristics of banks’ operational risk. After a spike following the great financial crisis, operational losses have declined in recent years. The spike is largely accounted for by losses due to improper business practices in large banks that occurred in the run-up to the crisis but were recognised only later. Operational value-at-risk can vary substantially – from 6% to 12% of total gross income – depending on the method used. It takes, on average, more than a year for operational losses to be discovered and recognised in the books. However, there is significant heterogeneity across regions and event types. For instance, improper business practices and internal fraud events take longer to be discovered. Operational losses are not independent of macroeconomic conditions and regulatory characteristics. In particular, we show that credit booms and periods of excessively accommodative monetary policy are followed by larger operational losses. Better supervision, on the other hand, is associated with lower operational losses. We provide an estimate of losses due to cyber events, a subset of operational loss events. Cyber losses are a small fraction of total operational losses, but can account for a significant share of total operational value-at-risk.

Suggested Citation

  • Gambacorta, Leonardo & Aldasoro, Inaki & Giudici, Paolo & Leach, Thomas, 2020. "Operational and cyber risks in the financial sector," CEPR Discussion Papers 14418, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:14418
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    Cited by:

    1. Md. Hamid Uddin & Md. Hakim Ali & Mohammad Kabir Hassan, 2020. "Cybersecurity hazards and financial system vulnerability: a synthesis of literature," Risk Management, Palgrave Macmillan, vol. 22(4), pages 239-309, December.
    2. Aldasoro, Iñaki & Gambacorta, Leonardo & Giudici, Paolo & Leach, Thomas, 2022. "The drivers of cyber risk," Journal of Financial Stability, Elsevier, vol. 60(C).
    3. Carletti, Elena & Claessens, Stijn & Fatás, Antonio & Vives, Xavier (ed.), 2020. "Barcelona Report 2 - The Bank Business Model in the Post-Covid-19 World," Vox eBooks, Centre for Economic Policy Research, number p329.
    4. Nenad Milojević & Srdjan Redzepagic, 2021. "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 10(3), pages 41-57.
    5. Rumyana Marinova, 2022. "Accounting Aspects of the Risk of Digital Payment Operations in Bulgarian Banks," Izvestia Journal of the Union of Scientists - Varna. Economic Sciences Series, Union of Scientists - Varna, Economic Sciences Section, vol. 11(2), pages 105-113, August.
    6. Eisenbach, Thomas M. & Kovner, Anna & Lee, Michael Junho, 2022. "Cyber risk and the U.S. financial system: A pre-mortem analysis," Journal of Financial Economics, Elsevier, vol. 145(3), pages 802-826.
    7. Iñaki Aldasoro & Sebastian Doerr & Leonardo Gambacorta & Sukhvir Notra & Tommaso Oliviero & David Whyte, 2024. "Generative artificial intelligence and cyber security in central banking," BIS Papers, Bank for International Settlements, number 145.
    8. Sebastian Doerr & Leonardo Gambacorta & Thomas Leach & Bertrand Legros & David Whyte, 2022. "Cyber risk in central banking," BIS Working Papers 1039, Bank for International Settlements.
    9. Ajjima Jiravichai & Ruth Banomyong, 2022. "A Proposed Methodology for Literature Review on Operational Risk Management in Banks," Risks, MDPI, vol. 10(5), pages 1-18, May.
    10. Cheng, Maoyong & Qu, Yang & Jiang, Chunxia & Zhao, Chenchen, 2022. "Is cloud computing the digital solution to the future of banking?," Journal of Financial Stability, Elsevier, vol. 63(C).
    11. Uddin, Md Hamid & Mollah, Sabur & Islam, Nazrul & Ali, Md Hakim, 2023. "Does digital transformation matter for operational risk exposure?," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    12. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.

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    More about this item

    Keywords

    Operational risks; Financial institutions; Cyber risks; Time to discovery; Value-at-risk;
    All these keywords.

    JEL classification:

    • D5 - Microeconomics - - General Equilibrium and Disequilibrium
    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • G2 - Financial Economics - - Financial Institutions and Services
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods

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