Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks
| dc.contributor.author | Appiahene, Peter | |
| dc.contributor.author | Missah, Yaw Marfo | |
| dc.contributor.author | Najim, Ussiph | |
| dc.contributor.orcid | 0000-0002-6098-4537 | |
| dc.contributor.orcid | 0000-0002-2926-681X | |
| dc.contributor.orcid | 0000-0002-6973-7495 | |
| dc.date.accessioned | 2023-12-06T14:07:18Z | |
| dc.date.available | 2023-12-06T14:07:18Z | |
| dc.date.issued | 2020 | |
| dc.description | An article published in Advances in Fuzzy Systems , Volume 2020, Article ID 8581202, 12 pages; https://doi.org/10.1155/2020/8581202 | |
| dc.description.abstract | %e 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. %is 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. %is 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). %e 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. %e 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. %e 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. %e 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. | |
| dc.description.sponsorship | KNUST | |
| dc.identifier.citation | Advances in Fuzzy Systems , Volume 2020, Article ID 8581202, 12 pages; https://doi.org/10.1155/2020/8581202 | |
| dc.identifier.uri | https://doi.org/10.1155/2020/8581202 | |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/14670 | |
| dc.language.iso | en | |
| dc.publisher | Advances in Fuzzy Systems | |
| dc.title | Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks | |
| dc.type | Article |