Aug 2026· Advances in Economics, Management and Political Sciences· 0 citations
TL;DR
This work compared five machine learning classifiers on a loan approval dataset: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine and applied SHAP TreeExplainer to interpret the best-performing model.
Abstract
Loan approval prediction is central to financial risk management, where lenders need models that are both accurate and interpretable. We compared five machine learning classifiers on a loan approval dataset: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine. The original 45,000-sample dataset was reduced to 20,000 for training due to computational constraints. We applied SHAP TreeExplainer to interpret the best-performing model. XGBoost achieved the highest AUC (0.9747) and accuracy (0.931). SHAP identified previous loan status, personal income, loan percentage, and loan interest rate as the top four features by importance. The analysis also traces how each feature shifts individual predictions toward approval or rejection. These findings give practitioners evidence for model selection in loan approval settings and produce explanations that meet regulatory transparency requirements.
Large volumes of loan applications motivate automated decision-support systems that can reduce processing delays and improve consistency while controlling credit risk. This study presents a unified supervised-learning framework comparing XGBoost, Gradient Boosting, and CatBoost for loan approval prediction. The experim...
N. Dandotiya, Kirti Jain, Prashant Kumar Shrivastava· 2026 International Conferenc...· 0 citations
A novel Ranked Attribute Selection with Midpoint Filtering with Midpoint Filtering (RASF) framework that extends LCS (EXTRACS) to enhance feature selection and rule validation for credit approval and supports explainable AI in credit scoring.
M. Ahamed, Abubakar Siddique, Trung Nguyen et al.· Proceedings of the Genetic a...· 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
A Standardized Multi-Metric Evaluation Framework is developed to support the selection of more objective and reproducible machine learning models in the case of loan approval to identify models that have a balance of performance and efficiency in loan approval experiments.
Trihartono Agus, Agus Ilyas Ilyas, S. Sattriedi et al.· Jurnal Informatika: Jurnal P...· 0 citations
Regression results reveal that profitability and ESG score significantly reduce credit risk and liquidity risk and monetary freedom increase credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh et al.· Risks· 0 citations
This study compares Random Forest (RF) and Extreme Gradient Boosting (XGBoost) for predicting loan defaults on 25,000 LendingClub loans from 2015–2016, using 14 financial features and 5-fold cross-validation. Both models perform similarly: RF reaches an AUC-ROC of 0.717, XGBoost reaches 0.715. RF does better on precisi...
Zi-Wei Feng· Advances in Economics, Manag...· 0 citations
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