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Predicting bank distress in the UK with machine learning

Author

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  • Suss, Joel

    (Bank of England)

  • Treitel, Henry

    (Bank of England)

Abstract

Using novel data and machine learning techniques, we develop an early warning system for bank distress. The main input variables come from confidential regulatory returns, and our measure of distress is derived from supervisory assessments of bank riskiness from 2006 through to 2012. We contribute to a nascent academic literature utilising new methodologies to anticipate negative firm outcomes, comparing and contrasting classic linear regression techniques with modern machine learning approaches that are able to capture complex non-linearities and interactions. We find the random forest algorithm significantly and substantively outperforms other models when utilising the AUC and Brier Score as performance metrics. We go on to vary the relative cost of false negatives (missing actual cases of distress) and false positives (wrongly predicting distress) for discrete decision thresholds, finding that the random forest again outperforms the other models. We also contribute to the literature examining drivers of bank distress, using state of the art machine learning interpretability techniques, and demonstrate the benefits of ensembling techniques in gaining additional performance benefits. Overall, this paper makes important contributions, not least of which is practical: bank supervisors can utilise our findings to anticipate firm weaknesses and take appropriate mitigating action ahead of time.

Suggested Citation

  • Suss, Joel & Treitel, Henry, 2019. "Predicting bank distress in the UK with machine learning," Bank of England working papers 831, Bank of England.
  • Handle: RePEc:boe:boeewp:0831
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Suss, Joel & Bholat, David & Gillespie, Alex & Reader, Tom, 2021. "Organisational culture and bank risk," Bank of England working papers 912, Bank of England.
    2. de Jesus, Diego Pitta & Besarria, Cássio da Nóbrega, 2023. "Machine learning and sentiment analysis: Projecting bank insolvency risk," Research in Economics, Elsevier, vol. 77(2), pages 226-238.
    3. Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022. "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, vol. 129(C).
    4. Umberto Collodel, 2021. "Finding a needle in a haystack: Do Early Warning Systems for Sudden Stops work?," Working Papers halshs-03185520, HAL.
    5. Umberto Collodel, 2021. "Finding a needle in a haystack: Do Early Warning Systems for Sudden Stops work?," PSE Working Papers halshs-03185520, HAL.
    6. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    7. Sanders, Austen & Willison, Matthew, 2021. "Measure for measure: evidence on the relative performance of regulatory requirements for small and large banks," Bank of England working papers 922, Bank of England.
    8. Caglayan, Mustafa & Pham, Tho & Talavera, Oleksandr & Xiong, Xiong, 2020. "Asset mispricing in peer-to-peer loan secondary markets," Journal of Corporate Finance, Elsevier, vol. 65(C).
    9. Buckmann, Marcus & Haldane, Andy & Hüser, Anne-Caroline, 2021. "Comparing minds and machines: implications for financial stability," Bank of England working papers 937, Bank of England.

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    More about this item

    Keywords

    Machine learning; bank distress; early warning system;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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