This study investigates the application of machine learning algorithms for predictive analytics in credit risk management, aiming to enhance the accuracy of predicting credit defaults. The research compares multiple machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, and LightGBM, using a real-world credit risk dataset. The study focuses on evaluating the models' performance based on metrics such as accuracy, precision, recall, and F1-score. The results show that ensemble models, particularly XGBoost and LightGBM, outperform traditional algorithms in terms of predictive accuracy and computational efficiency, demonstrating their ability to effectively handle complex datasets. The comparative analysis highlights the strengths and weaknesses of each model, providing insights into the trade-offs between interpretability and predictive power. XGBoost and LightGBM are found to be highly effective for credit risk prediction, though challenges such as model interpretability and overfitting remain. The findings suggest that machine learning offers a promising approach for improving credit risk management, with implications for the financial industry to make more informed, data-driven lending decisions. The study underscores the importance of addressing interpretability concerns and data quality issues in real-world applications, paving the way for future advancements in machine learning for credit risk prediction. 

machine learning, credit risk management, predictive analytics

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