Predicting bank distress in the UK with machine learning

Joel Suss () and Henry Treitel ()
Additional contact information
Joel Suss: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH
Henry Treitel: Bank of England, Postal: Bank of England, Threadneedle Street, London, EC2R 8AH

No 831, Bank of England working papers from 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.

Keywords: Machine learning; bank distress; early warning system (search for similar items in EconPapers)
JEL-codes: C14 C33 C52 C53 G21 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2019-10-04
New Economics Papers: this item is included in nep-big, nep-cfn, nep-cmp, nep-fmk, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://www.bankofengland.co.uk/-/media/boe/files/ ... machine-learning.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boe:boeewp:0831

Access Statistics for this paper

More papers in Bank of England working papers from Bank of England Bank of England, Threadneedle Street, London, EC2R 8AH. Contact information at EDIRC.
Bibliographic data for series maintained by Digital Media Team ().