Abstract
The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.
Highlights
Introduction e financial crisis that hitGhana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry
Machine learning algorithms have been viewed as a good tool to approximate numerous nonparametric and nonlinear problems [5]. is means that the banking industry provides good opportunities for the applications of a combined Data Envelopment Analysis (DEA) and machine learning models. ere are few literatures dealing with developing country bank branch efficiency using DEA and machine learning algorithms. is paper presents a combined DEA and three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches. e results were compared with the corresponding efficiency ratings obtained from CRS DEA
Just 33(7.43%) bank branches had an efficiency score of between 80% to 99%, 1 (0.23%) bank branches had efficiency score of between 70 and 79, 21 (4.73%) had efficiency score of between 60 and 69, 19 (4.28%) had between 50 to 59, and 356 (80.18%) had an efficiency score below 50%. is 356 (80.18%) number of bank branches confirms the fact that a lot of Ghanaian banks are not efficient in using their resources to collect deposits from customers as most banks were struggling to meet the minimum capital requirements set by the central bank in 2017 [3, 54, 55]
Summary
Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and restore customers’ confidence, efficiency, and performance analysis in the banking industry has become a hot issue. DEA models share some similarities with machine learning algorithms. Is means that the banking industry provides good opportunities for the applications of a combined DEA and machine learning models. Ere are few literatures dealing with developing country bank branch efficiency using DEA and machine learning algorithms. Is paper presents a combined DEA and three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches. The prediction accuracies of the three machine learning algorithm models were compared. e motivation behind this study is the fact that the DEA property of unit invariant is similar to the property of scale preprocessing required by machine learning algorithms such as NNs. is validates the rationale to compare the results of pure DEA and DEAmachine learning algorithm model results
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