An Intelligent Model to Assess the Credit Risk in Egyptian Banks | ||||
| المجلة العلمية للبحوث والدراسات التجارية | ||||
| Volume 38, Issue 1, March 2024, Page 1749-1782 PDF (781.39 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/sjrbs.2024.272113.1637 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Khaled Fathy  1; Mohamed Marie 2; Engy Yehia  3
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| 1Business Information Systems Department, Faculty of Commerce and Business Administration, Helwan University, Helwan, Cairo, Egypt. | ||||
| 2Information Systems Department, Faculty of Computers and Artificial Intelligence, Helwan University, Helwan, Cairo, Egypt. | ||||
| 3Faculty of Commerce and Business Administration at Helwan University | ||||
| Abstract | ||||
| In the realm of financial and banking institutions, the art of forecasting and assessing banking risks holds paramount significance. Among these risks, the assessment of banking credit risks looms large in contemporary times, given the heightened necessity for decision-makers to anticipate the likelihood of loan defaults. However, one formidable challenge persists: the inadequate assessment of banking credit risks. This challenge stems from the multifaceted factors that influence risk assessment and the soundness of credit decisions. In response to this pressing issue, our research presents a predictive model employing machine learning (ML) algorithms. Our objective is to facilitate informed credit decision-making and safeguard the financial assets of banks. In pursuit of this aim, we employed five machine learning classification algorithms: Artificial Neural Networks (ANN), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT) and XGBoost (XGB). To ensure the robustness of our study, we utilized a real-world dataset gleaned from the historical records of a prominent Egyptian bank. Subsequently, we assessed the performance of our model based on key metrics such as accuracy, precision, recall, and the f1 score. The results showed that XGB exhibited the highest accuracy, underlining the potential for ML algorithms to revolutionize the assessment of banking credit risks. | ||||
| Keywords | ||||
| Machine learning; Financial institutions; Risk assessment; Loan defaults; Predictive model | ||||
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