An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction

Credit scoring is vital in the financial industry,assessing the risk of lending to credit card applicants. Traditionalcredit scoring methods face challenges with large datasets anddata imbalance between creditworthy and non-creditworthy applicants.This paper introduces an advanced machine learningand deep learning framework to improve the accuracy andreliability of credit card approval predictions. We utilized extensivedatasets of user application records and credit history,implementing a comprehensive preprocessing strategy, featureengineering, and model integration. Our methodology combinesneural networks with an ensemble of base models, includinglogistic regression, support vector machines, k-nearest neighbors,decision trees, random forests, and gradient boosting. The ensembleapproach addresses data imbalance using Synthetic MinorityOver-sampling Technique (SMOTE) and mitigates overfittingrisks. Experimental results show that our integrated modelsurpasses traditional single-model approaches in precision, recall,F1-score, AUC, and Kappa, providing a robust and scalablesolution for credit card approval predictions. This researchunderscores the potential of advanced machine learning techniquesto transform credit risk assessment and financial decisionmaking.