Predicting Credit Card Approval Using Machine Learning Techniques

Abstract

Predicting credit card approvals is crucial for financial institutions to streamline decision-making and mitigate risks. This study applies advanced machine learning techniques, including Logistic Regression, Decision Tree Classifier, Random Forest Classifier, and XGBoost Classifier, to a dataset comprising applicants' demographic, financial, and credit history information. After preprocessing and hyperparameter tuning using Random Search, XGBoost achieved the best performance with 99.04% accuracy, 85% recall, and 78% precision on the test data. The results demonstrate that ensemble methods like XGBoost and Random Forest outperform simpler models, achieving strong generalization and predictive accuracy. These findings highlight the effectiveness of advanced machine learning models for optimizing credit card approval systems.

References

[1] Farzad G, Roshdieh N. The Interplay of Destructive Work Behaviors, Organizational Citizenship Behaviors, and Fiscal Decentralization: Implications for Economic Development in Developing Countries. International Research Journal of Economics and Management Studies IRJEMS. 2024;3(8).

[2] Izadian N, Sanaei F, Sadeghi S, Khorsand MS, Shakerinasab N. Market reactions to capital increases: Insights from asset revaluation in pharmaceutical companies on the stock exchange. World Journal of Advanced Research and Reviews. 2024;23(2):2486-97.

[3] Talebzadeh H, Fattahiamin A, Talebzadeh M, Sanaei F, Moghaddam PK, Espahbod S. Optimizing Supply Chains: A Grey-DEMATEL Approach to Implementing LARG Framework. Teh. Glas. 2024;19:1-8.

[4] Esmaeili A, Mtibaa A. SERENE: A Collusion Resilient Replication-based Verification Framework. arXiv preprint arXiv:2404.11410. 2024 Apr 17.

[5] Latifi K, Ebrahimi A, Ranjbaran M, Mirzaei A, Fakhri Z. Efficient customer relationship management systems for online retailing: The investigation of the influential factors. Journal of Management & Organization. 2023 Jul;29(4):763-98.

[6] Hamid FS, Loke YJ. Financial literacy, money management skill and credit card repayments. International Journal of Consumer Studies. 2021 Mar;45(2):235-47.

[7] Surekha M, Umesh U, Dhinakaran DP. A study on utilization and convenient of credit card. Journal of Positive School Psychology. 2022 May 7:5635-45.

[8] Owrang O., M. M., Schwarz, G., & Horestani, F. J. (2025). Prediction of Breast Cancer Recurrence With Machine Learning. In M. Khosrow-Pour, D.B.A. (Ed.), Encyclopedia of Information Science and Technology, Sixth Edition. Advance online publication. https://doi.org/10.4018/978-1-6684-7366-5.ch061

[9] Attri I, Awasthi LK, Sharma TP. Machine learning in agriculture: a review of crop management applications. Multimedia Tools and Applications. 2024 Feb;83(5):12875-915..

[10] Daoutidis P, Lee JH, Rangarajan S, Chiang L, Gopaluni B, Schweidtmann AM, Harjunkoski I, Mercangöz M, Mesbah A, Boukouvala F, Lima FV. Machine learning in process systems engineering: Challenges and opportunities. Computers & Chemical Engineering. 2024 Feb 1;181:108523.

[11] Karami M, Hamzehei S, Rastegari F, Akbarzadeh O. Exploring the Relationship Between Air Pollution and CNS Disease Mortality in Italy: A Forecasting Study with ARIMA and XGBoost. In2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) 2023 Jul 24 (pp. 46-52). IEEE.

[12] Sadeghi S, Niu C. Augmenting Human Decision-Making in K-12 Education: The Role of Artificial Intelligence in Assisting the Recruitment and Retention of Teachers of Color for Enhanced Diversity and Inclusivity. Leadership and Policy in Schools. 2024 May 26:1-21.

[13] Guo L, Song R, Wu J, Xu Z, Zhao F. Integrating a machine learning-driven fraud detection system based on a risk management framework. Applied and Computational Engineering. 2024 Jul 31;87:80-6.

[14] Chen Y, Calabrese R, Martin-Barragan B. Interpretable machine learning for imbalanced credit scoring datasets. European Journal of Operational Research. 2024 Jan 1;312(1):357-72.

[15] Del Pilar EC, Bongo MF. Towards the Improvement of Credit Card Approval Process Using Classification Algorithm. In2023 8th International Conference on Business and Industrial Research (ICBIR) 2023 May 18 (pp. 461-465). IEEE.

[16] Uddin N, Ahamed MK, Uddin MA, Islam MM, Talukder MA, Aryal S. An ensemble machine learning based bank loan approval predictions system with a smart application. International Journal of Cognitive Computing in Engineering. 2023 Jun 1;4:327-39.

[17] Viswanatha V, Ramachandra AC, Vishwas KN, Adithya G. Prediction of Loan Approval in Banks Using Machine Learning Approach. International Journal of Engineering and Management Research. 2023 Aug 2;13(4):7-19.

[18] https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction/data