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.