Jul 2026· Proceedings of the Genetic and Evolutionary Computation Conference Companion· 0 citations· 26 references
TL;DR
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.
Abstract
Machine learning is widely used to improve predictive accuracy in complex domains like credit scoring, but many models (e.g., deep neural networks) remain opaque. This lack of interpretability is problematic in regulated domains (banking, finance) where transparency is required. Rule-based learning methods, such as Learning Classifier Systems (LCS), offer a trade-off between accuracy and explainability. We introduce a novel Ranked Attribute Selection with Midpoint Filtering (RASF) framework that extends LCS (EXTRACS) to enhance feature selection and rule validation for credit approval. RASF first ranks features by mutual information, then employs rank-guided randomized selection to diversify rule conditions, and finally filters new rules by midpoint-based Euclidean distance to the current instance. We evaluate RASF-enhanced LCS on public loan approval datasets, comparing against a baseline LCS and logistic regression. Results show that RASF improves predictive accuracy by about 3.6 – 4.64% over the base LCS, while producing an inherently interpretable rule set. By bridging accuracy and transparency, RASF-LCS supports explainable AI in credit scoring.
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
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.
Shengze Xu· Advances in Economics, Manag...· 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
Machine learning is widely used in credit scoring, but many high-performing models lack interpretability, limiting their use in regulated domains. Rule-based approaches such as Learning Classifier Systems (LCS) offer a balance between accuracy and explainability. This paper introduces Ranked Attribute Selection with Midpoint Filtering (RASF), an extension to LCS that enhances feature selection and rule validation. RASF combines mutual information-based feature ranking, guided attribute selection, and distance-based rule filtering. Experiments on loan approval datasets show that RASF improves accuracy by 3–5% over standard LCS while maintaining interpretable, rule-based outputs. These results highlight the potential of RASF-LCS for explainable credit decision-making.
Ahamed Zahvie, Abubakar Siddique, Trung Nguyen et al.· Proceedings of the Genetic a...· 1 citation
Although eXplainable Artificial Intelligence (XAI) has significantly increased interest in recent years, its application in credit risk classification (CRC) still requires further exploration to evaluate its effectiveness in classifying loan applicants. In credit risk classification, the application of deep learning models requires an explanation of their outputs to make the decision-making process transparent. However, existing explainable credit risk classification models often use a single classifier, which can degrade the prediction accuracy and reduce the quality of explanations. In order to improve both explainability and accuracy, we design a new explainable ensemble deep learning model for credit risk classification (EED-CRC). The proposed model involves two main phases: model building and model interpretation. In the first phase, we propose an ensemble CRC model that uses three Convolutional Neural Network (CNN) architectures to classify applicants. The outputs of the three CNN architectures are then integrated using a machine learning model, yielding more accurate predictions than those obtained from any individual classifier. In the second phase, we aim to interpret the obtained classification model through three XAI methods, such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Permutation Feature Importance (PFI), providing valuable insights to decision makers for classifying applicants. Experimental results on three real credit risk datasets show that the EED-CRC approach achieves superior performance compared to traditional CRC methods, both in accuracy and in explainability.
Unknown authors· International Journal of Inf...· 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