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This is a Quantitative Study study.

Loan Default Prediction Using Machine Learning Algorithms

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LightGBM achieved the highest accuracy of 0.9764 for predicting loan default, indicating its effectiveness.
Feature importance analysis revealed that interest and credit type are among the key predictors of loan default.
Model performance was evaluated through accuracy, precision, and recall, emphasizing comprehensive assessment metrics.
Synthetic Minority Over-sampling Technique (SMOTE) was employed to address class imbalance during the model training.

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This study evaluates various machine learning algorithms for loan default prediction, suggesting LightGBM may enhance risk assessment.

Kang et al. (2025) studied this question.

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