Credit risk is one of the most fundamental challenges faced by financial institutions and banks in the loan approval process. Inaccurate assessment of borrowers' repayment capacity can lead to an increase in non-performing loans and impose significant financial losses on the banking system. Therefore, the application of advanced data analysis techniques and machine learning algorithms for accurate credit risk prediction has gained considerable importance.

In this study, a credit risk assessment dataset was utilized, and a comprehensive data preprocessing procedure was performed. This process included handling and imputing missing values, identifying and removing outliers, normalizing numerical features, and encoding categorical variables to improve data quality for model training. Subsequently, three widely used machine learning algorithms, namely Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine (SVM), were trained to predict loan repayment status.

To evaluate the performance of the proposed models, several metrics were employed, including Accuracy, Precision, Recall, F1-Score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), providing a comprehensive and multidimensional comparison of their predictive capabilities. The experimental results demonstrated that the XGBoost model outperformed the other models across most evaluation metrics and exhibited superior ability in correctly identifying both high-risk and low-risk customers. Based on these findings, it can be concluded that gradient boosting–based algorithms, particularly XGBoost, represent an efficient and reliable approach for credit risk prediction in financial institutions.