Aug 2026· Advances in Economics, Management and Political Sciences· Vol 292, pp. 24-32· 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 experiments use the Dream Housing Finance dataset containing 614 applications and 12 predictive variables after removing Loan_ID. The pipeline includes missing-value treatment, feature engineering, scaling, SMOTE-based class balancing applied only to training data, and evaluation on a held-out test set of 169 samples. Perfect training performance is treated as a diagnostic warning rather than evidence of generalization. CatBoost achieved the best held-out accuracy (88.17%), precision (88.37%), recall (88.37%), and F1-score (88.37%), with 10 false approvals and 10 false rejections. Confusion-matrix analysis, false-positive and false-negative rates, balanced accuracy, and Wilson confidence intervals indicate the most balanced performance among the evaluated models. The framework is intended as a prototype decision-support approach; larger multi-institutional validation, probability-based discrimination analysis, explainability, calibration, and fairness assessment are required before deployment in real lending environments.
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 financial decision-making.
Muskan, B. Sidhu· International Journal of Com...· 0 citations
The growing need for accurate, consistent, and reliable loan approval systems The use of machine learning in credit decision-making is increasingly important for financial institutions, but comparative research still often focuses on Accuracy or a limited number of classification metrics, so the trade-off between predictive performance and computational efficiency is not fully described. This study aims to develop a Standardized Multi-Metric Evaluation Framework (MMEF) to support the selection of more objective and reproducible machine learning models in the case of loan approval. The research method uses a standardized experimental pipeline with consistent preprocessing, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), identical data sharing, model optimization, and multi-metric evaluation. Five algorithms, namely Logistic Regression, Support Vector Machine (SVM), Random Forest, XGBoost, and CatBoost, are compared using a loan approval dataset consisting of 45,000 records and 13 predictor features. The evaluation includes Accuracy, Precision, Recall, F1-score, ROC-AUC, training time, and Overall Score. The results show that XGBoost provides the best overall performance with Accuracy 87.86%, Precision 66.77%, Recall 90.30%, F1-score 76.77%, ROC-AUC 96.27%, and Overall Score 0.827503. CatBoost has the fastest training time of 1.01 seconds, while SVM obtained the highest Recall of 92.40% with a much longer training time. These results indicate that model selection is not sufficient based on a single metric. MMEF provides a more systematic evaluation basis 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
Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
Jamil 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 precision (0.572 vs. 0.504), while XGBoost leads on recall (0.164 vs. 0.116) and F1-score (0.247 vs. 0.193). The gap is small enough that the choice should rest on operational priorities rather than accuracy: Random Forest for interpretability, XGBoost for recall.
Zi-Wei Feng· Advances in Economics, Manag...· 0 citations
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