An Integrated Machine Learning and Deep Learning Framework for Credit Card Approval Prediction
Journal:
arXiv
Published Date:
Sep 25, 2024
Abstract
Credit scoring is vital in the financial industry, assessing the risk of
lending to credit card applicants. Traditional credit scoring methods face
challenges with large datasets and data imbalance between creditworthy and
non-creditworthy applicants. This paper introduces an advanced machine learning
and deep learning framework to improve the accuracy and reliability of credit
card approval predictions. We utilized extensive datasets of user application
records and credit history, implementing a comprehensive preprocessing
strategy, feature engineering, and model integration. Our methodology combines
neural networks with an ensemble of base models, including logistic regression,
support vector machines, k-nearest neighbors, decision trees, random forests,
and gradient boosting. The ensemble approach addresses data imbalance using
Synthetic Minority Over-sampling Technique (SMOTE) and mitigates overfitting
risks. Experimental results show that our integrated model surpasses
traditional single-model approaches in precision, recall, F1-score, AUC, and
Kappa, providing a robust and scalable solution for credit card approval
predictions. This research underscores the potential of advanced machine
learning techniques to transform credit risk assessment and financial
decision-making.
