Impact of Social Responsibility Indicators on the Technical Efficiency of Banks Using Machine Learning

Abstract

Social responsibility (SR) implies the responsibility of firms obliged toward society from which various institutions derive the benefits. The present work aimed to analyze the impact of SR on the technical efficiency (TE) of public sector banks (PSBs) with artificial intelligence podiums. It also identified an important SR indicator inclusive of four dimensions. Mathematically extracting the importance of each parameter related to the efficiency metrics is tedious. Therefore, supervised machine learning (ML) algorithms like random forest (RF) and XGBoost (XGB) were applied in this study. Furthermore, banks' effective implementation of SR policy for sustainable development was discussed based on ML. In this study, the impact of 46 social responsibility indicators on the technical efficiency of PSBs was investigated using ML and non-parametric approach. Furthermore, the present chapter added to the body of literature by analyzing which indicator of SR better influenced technical efficiency of PSBs using ML. The results revealed that an important indicator that impacted efficiency concerning constant and variable return to scale is an area of specialization and background of employees working in PSBs. This depicted that PSBs implied to work on the efficiency of its employees in terms of social responsibility.