---
title: Predicting Bank Distress in Europe: Using Machine Learning and a Novel Definition of Distress
url: https://www.ml-quant.com/papers/ssrn/5098026/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 5098026
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5098026
featured: 2025-01-23
citations: 1
topic: Risk, Credit & Banking
---


# Predicting Bank Distress in Europe: Using Machine Learning and a Novel Definition of Distress

The paper presents a machine learning-based early warning system to predict distress in large European banks, with the random forest model performing best.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5098026
- Identifier: SSRN 5098026
- Released: 2025-01-15
- First featured: Quant Letter No. 83 (2025-01-23): https://www.ml-quant.com/issues/2025-01-23/
- Citations (Semantic Scholar): 1
- Published in: not yet
- Topic: Risk, Credit & Banking

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