EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks

Tamás Kristóf () and Miklós Virág

Research in International Business and Finance, 2022, vol. 61, issue C

Abstract: This article provides evidence that machine learning methods are suitable for reliably predicting the failure risk of European Union-27 banks from the experiences of the past decade. It demonstrates that earnings, capital adequacy, and management capability are the strongest predictors of bank failure. Critical and relevant field research is presented in the context of economic uncertainties arising from the COVID-19 pandemic. The results suggest that the developed models possess high predictive power, with the C5.0 decision tree model providing the best performance. The findings have policy implications for bank supervisory authorities, bank executives, risk management professionals, and policymakers working in finance. The models can be used to recognize bank weaknesses in time to take appropriate mitigating actions.

Keywords: Bank failure; Classification; Credit risk modeling; Machine learning (search for similar items in EconPapers)
JEL-codes: C38 C45 C53 G17 G21 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:61:y:2022:i:c:s0275531922000320

DOI: 10.1016/j.ribaf.2022.101644

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