Comparative Analysis of Machine Learning Models for Credit Risk Prediction in Banking Systems.

The increasing complexity of credit risk management in banking systems has led to the adoption of machine learning techniques to improve the prediction of loan defaults. This study evaluates and compares the performance of several machine learning models—Logistic Regression, Random Forest, Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Neural Networks—in predicting credit risk. The models were tested on a comprehensive dataset containing demographic, financial, and historical loan data. Performance was assessed based on accuracy, precision, recall, F1-score, AUC, and confusion matrix analysis. The results indicate that Gradient Boosting (XGBoost) outperformed the other models with the highest accuracy (88.7%), precision (89.5%), recall (80.3%), and AUC (91.3%), demonstrating its superior ability to predict loan defaults and manage credit risk effectively. Random Forest followed closely in performance, while Logistic Regression showed solid results with a focus on interpretability. Neural Networks and SVM performed well in accuracy but were more resource-intensive and less interpretable. The study concludes that Gradient Boosting (XGBoost) is the most suitable model for large-scale credit risk management due to its balance of high predictive power and ability to handle complex, imbalanced datasets. However, the choice of model should consider computational resources, interpretability requirements, and specific operational constraints of the banking institution.