Predicting ESG Controversies in Banks Using Machine Learning Techniques

Anna Rita Dipierro, Fernando Jimenéz Barrionuevo and Pierluigi Toma

Corporate Social Responsibility and Environmental Management, 2025, vol. 32, issue 3, 3525-3544

Abstract: Mistreating environmental, social, and governance (ESG) concerns has serious drawbacks in organizations of any type, and even more in banks. Deeply revolutionized in its taxonomy of risks, banking sector is herein evaluated in its integration of ESG parameters that, when lacking, leads to ESG‐related controversies (ESGC). Thereby, this research approaches the almost uncharted territory of ESGC in banks, by means of machine learning. Aiming at selecting the set of features that are relevant in ESGC prediction, techniques belonging to feature selection are used over a real panel dataset of 140 banks evaluated for a wide set of features over 2011–2020 time‐span. We find the power that governance‐employees dynamics detains in making out‐of‐sample predictions and forecasting of ESGC banks' risk. Finally, we provide implications for researchers, practitioners and regulators, further confirming the need for the rapid inroads that machine learning tools are actually making in the banking toolkit and in the regulatory technology.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/csr.3146

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:wly:corsem:v:32:y:2025:i:3:p:3525-3544

Access Statistics for this article

More articles in Corporate Social Responsibility and Environmental Management from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().