Machine Learning for Credit Approval: Enhancing Decision Accuracy and Explainability

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.