Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311181 
Year of Publication: 
2025
Series/Report no.: 
EBA Staff Paper Series No. 21
Publisher: 
European Banking Authority (EBA), Paris La Défense
Abstract: 
This paper develops an early warning system for predicting distress for large European banks. Using a novel definition of distress derived from banks' headroom above regulatory requirements, we investigate the performance of three machine learning techniques against the traditional logistic model. We find that the random forest model shows superior performance both out-of-sample and out-oftime. Unlike previous studies, we also employ a series of sampling techniques showing that they significantly improve the ability to identify distress events irrespective of the model used. Moreover, we show that ensemble techniques can help improve performance relative to the single best performing model. Finally, using the latest machine learning interpretability tools, we show that the variables closely tied to bank profitability and solvency are important drivers for predicting bank distress. Overall, our paper has important practical implications for bank supervisors and macroprudential authorities who can utilise our findings to identify bank weaknesses ahead of time and adopt pre-emptive measures to safeguard financial stability.
Subjects: 
Bank distress
early warning system
machine learning
neural networks
decision tree
random forest
riskassessment
banking supervision
JEL: 
C14
C33
C38
C45
C52
C53
G21
Persistent Identifier of the first edition: 
ISBN: 
978-92-9245-978-9
Document Type: 
Working Paper

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