Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 12 references
Abstract
This study addresses the prediction of credit default risk using the Home Credit Default Risk dataset. Due to the approximately eight percent imbalanced class distribution in the data structure, an intensive feature engineering pipeline that reduces multiple tables to the customer level was applied before modeling. The implementation was designed to include behavioral summaries generated from external credit history, previous applications, installment behavior, credit card transactions, and POS cash records. Missing value handling, encoding, and multicollinearity reduction steps were performed on the obtained features. LightGBM was selected as the classification model, and the hyperparameters were optimized with Optuna under cross validation. In the best configuration, the mean AUC value was observed as 0.78682 and the mean PR AUC value as 0.28015. In order for the probabilities to be used more reliably in decision making processes, Platt scaling and SHAP based explainability analysis were applied.
The findings indicate that interpretable machine learning can give lenders a defensible account of every credit decision — a capability that serves both regulatory oversight and the individuals whose applications are under review.
Kelvin Lin· Advances in Economics, Manag...· 0 citations
The study demonstrates that credit-risk models should be selected based on balanced default detection rather than overall accuracy alone and demonstrates that the SVM model trained without standardization failed to identify default clients effectively.
Andre Pratama Adiwijaya· International Journal for Sc...· 0 citations
Research on bond default risk prediction based on the XGBoost model shows that the sustainability of corporate profitability is the core influencing factor of default, providing methodological support for the prevention and control of bond default risk.
Overall, it can be concluded that the application of gradient-boosted tree ensembles in combination with good feature engineering and hyperparameter optimization makes it possible for such models to provide an optimal trade-off between accuracy, speed, and explainability when predicting housing loan default.
K. Bhavani, Vijinigiri Vinay Kumar· International Scientific Jou...· 0 citations
The results show that introducing big data variables and adopting the XGBoost model can significantly improve the accuracy and AUC value of default prediction, effectively reducing the credit default risk of commercial banks.
Jia-Cheng Yan· Asia Pacific Economic and Ma...· 0 citations
This study explores the use of Explainable Artificial intelligence techniques to improve the interpretability of credit default prediction and highlights the practical value of explainable machine learning in developing more understandable, trustworthy, and accountable credit risk assessment systems for real-world fina...
Muskan, B. Sidhu· International Journal of Com...· 0 citations
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