Performance evaluation of Indian banks using feature selection data envelopment analysis: A machine learning perspective

Abstract

The early signal of the potential risk of bank failure is imperative for various stakeholders such as management personnel, lenders, and shareholders. This study has developed a new feature selection‐based data envelopment analysis (DEA) model to calculate the efficiencies and predict the stress of Indian banks. The feature selection‐based data envelopment analysis (DEA) model combines feature selection methodology (a machine learning technique), with the traditional data envelopment analysis. The proposed model is able to map the DEA ranks, and the status of both failure and success of banks, and other similar decision‐making units (DMUs). It also helps in solving the problems associated with the selection of appropriate input and output features as well as the time‐dependent data points which usually have a lagged effect. The proposed model is applicable only when appropriate samples of past data of DMUs performance are available, whereby it maps both the input and output features with performance. Importantly, while dealing with the past data for selecting the appropriate inputs and outputs, it is imperative to select all the indicators that affect the performance of DMUs and thereby reduce the number of features using the machine learning approach. The proposed model is one of its kind, integrating a machine learning technique to a nonparametric frontier decision model.

Integration of machine learning in the supply chain for decision making: A systematic literature review

“…The research results provide indications for choosing the ML approach that promises the best result for cost estimates and cost structure information to support decision-making in buyer-supplier relationships. Finally, Kumar, Shrivastav and Mukherjee (2022)…”