Predicting Efficiency of Commercial Banks in Vietnam: A DEA and Machine Learning Approach
DOI:
https://doi.org/10.47654/v28y2024i4p%25pKeywords:
Vietnamese commercial banks, Efficiency, Data Envelopment Analysis, Machine learningAbstract
Purpose: This study investigates the effectiveness of a hybrid Data Envelopment Analysis (DEA) and Machine Learning (ML) approach in predicting the efficiency of commercial banks in Vietnam.
Methodology: A two-stage model is proposed. First, DEA is employed to evaluate bank efficiency from 2012 to 2021 by using data from annual reports. Second, various ML algorithms (ANN-MLP, linear regression, and random forest) are used to forecast efficiency scores based on the DEA results. The performance of each ML model is compared to identify the most effective approach.
Findings: The findings show that the ANN-MLP model significantly outperforms Linear Regression and Random Forest models, achieving the lowest Root Mean Squared Error (0.0627), the lowest Mean Absolute Error (0.0492), and the highest R-squared value (0.8352) in predicting bank efficiency scores.
Research limitations/implications: The study utilizes data from a specific timeframe and may be limited by potential inaccuracies in financial statements. Future research could extend the period and explore additional data sources.
Practical implications: The proposed DEA-ML model can be a valuable tool for bank managers and policymakers to assess and predict bank efficiency, ultimately leading to improved decision-making, greater efficiency, and enhanced competitiveness. The findings might be generalizable to other bank types in similar contexts.
Social implications: This research contributes to developing DEA-ML models, potentially influencing practices in bank efficiency measurement and leading to a more robust financial system.
Originality/value: This study is among the first to integrate DEA with ML for predicting bank efficiency in the Vietnamese context. This study also contributes to Decision Sciences by developing and validating a hybrid predictive framework that enhances managerial decision-making for performance benchmarking and strategic resource allocation in the banking sector.
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