Work

Predicting ESG Controversies in Banks Using Machine Learning Techniques

Year: 2025

Type: article

Abstract: 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.

Source: Corporate Social Responsibility and Environmental Management

DOI: 10.1002/csr.3146

Authors: Anna Rita Dipierro, Fernando Jimenéz Barrionuevo, Pierluigi Toma

Institutions: University of Salento, Universidad de Murcia

Language: English

Topic: Corporate Social Responsibility Reporting

Subfield: Strategy and Management

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $4,100

Funders: European Commission, Università della Calabria