Predicting Super-Efficiency of Commercial Bank Branches Using Regression Models

Authors

  • Fateme Gerami * Department of Electrical Engineering, Faculty of Electrical Engineering, Jundi Shapur University of Technology, Dezful, Iran.
  • Shahla Gerami Department of Electrical Engineering, Faculty of Electrical Engineering, Jundi Shapur University of Technology, Dezful, Iran.

https://doi.org/10.48314/ijorai.v1i2.61

Abstract

This study focuses on predicting the super-efficiency scores of commercial bank branches by employing various regression models. The analysis is conducted on a dataset comprising 375 bank branches from the fiscal year 2017, utilizing a range of financial, operational, and cost-related indicators as input features. A suite of regression techniques, including linear regression, ensemble methods such as Random Forest and XGBoost, as well as neural network models, is implemented to estimate the super-efficiency values. Model performance is assessed through metrics including Mean Absolute Error (MAE) and the coefficient of determination (R²). The findings reveal that non-linear models, especially ensemble-based algorithms, outperform linear models in terms of accuracy and generalizability. This regression framework offers a robust decision-support tool for evaluating and benchmarking the operational efficiency of bank branches.

Keywords:

Data envelopment analysis, Machine learning, Commercial banks, Bank branch performance evaluation

References

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Published

2025-06-04

Issue

Vol. 1 No. 2 (2025): International Journal of Operations Research and Artificial Intelligence (IJORAI)

Section

Articles

How to Cite

Gerami, F. ., & Gerami, S. . (2025). Predicting Super-Efficiency of Commercial Bank Branches Using Regression Models. International Journal of Operations Research and Artificial Intelligence , 1(2), 54-60. https://doi.org/10.48314/ijorai.v1i2.61

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