Predicting Efficiency of Commercial Banks in Vietnam: A DEA and Machine Learning Approach

Quang Hung Do

doi:10.47654/v28y2024i4p%p

Authors

Quang Hung Do Posts and Telecommunications Institute of Technology Corresponding Author

DOI:

https://doi.org/10.47654/v28y2024i4p%25p

Keywords:

Commercial banks, bank efficiency, Data Envelopment Analysis, Machine Learning (ML)

Abstract

Motivated by advances in Data Envelopment Analysis (DEA) and Machine Learning (ML) methods, the objective of this work is to provide an applicable technique for evaluating and predicting commercial bank efficiency in the context of Vietnam. In this sense, a two-stage hybrid model is developed to employ ML via integration with DEA, which is used as a preprocessor, to investigate the ability of the DEA-ML approach to estimate the efficiency of commercial banks in Vietnam. The collected data are from the published annual reports of the commercial banks operating in Vietnam in the period of 2012-2021. First, DEA was utilized to estimate the efficiency of banks over the period. Then, different ML techniques, including Multilayer Perceptron Neural Network (ANN-MLP), linear regression, and Random Forest were used to predict the efficiency score. Our empirical results indicate that the ANN-MLP model is more suitable for predicting bank efficiency.

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