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Predicting ESG controversies in banks using machine learning techniques

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dc.contributor.authorDipierro, Anna Rita
dc.contributor.authorJiménez Barrionuevo, Fernando
dc.contributor.authorToma, Pierluigi
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2025-12-18T07:03:38Z
dc.date.available2025-12-18T07:03:38Z
dc.date.copyright© 2025 The Author(s)
dc.date.issued2025-02-04
dc.description.abstractMistreating 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.
dc.formatapplication/pdf
dc.format.extent20
dc.identifier.citationCorporate Social Responsibility and Environmental Management, 2025, Vol. 32, Issue 3, pp. 3525-3544
dc.identifier.doihttps://doi.org/10.1002/csr.3146
dc.identifier.eissn1535-3966
dc.identifier.issn1535-3958
dc.identifier.urihttp://hdl.handle.net/10201/181289
dc.languageeng
dc.publisherWiley
dc.relationOpen access publishing facilitated by Universita della Calabria, as part of the Wiley - CRUI-CARE agreement.
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBanks
dc.subjectControversies
dc.subjectESG
dc.subjectGovernance
dc.subjectMachine learning
dc.subjectRisk
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titlePredicting ESG controversies in banks using machine learning techniques
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublication47706328-5460-433a-b86f-dcbcc9841c53
relation.isAuthorOfPublication.latestForDiscovery47706328-5460-433a-b86f-dcbcc9841c53
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