An Improved BP Neural Network Credit Default Prediction Model Based on SMOTE and Random Forests
Abstract
against several baseline classifiers, including logistic regression, XGBoost, LightGBM, CatBoost, and a stacking ensemble method. The
outcomes of the final test data indicate that the proposed method surpasses the alternatives regarding AUC, KS, and accuracy, with an AUC of
0.714, a KS value of 0.821, and an accuracy of 80.3%. The results demonstrate that the model markedly improves the capacity to distinguish
between default and non-default instances, while preserving elevated overall predictive accuracy.
Keywords
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DOI: http://dx.doi.org/10.70711/frim.v3i7.6820
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