Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework
Abstract
This study introduces a multi-stage cascaded machine learning framework for loan approval prediction, which addresses the inefficiencies inherent in traditional processes. The framework combines Gradient Boosting, Support Vector Machine, and XGBoost to refine predictions at each stage. This approach allows the framework to leverage the strengths of each technique in order to handle complex relationships and imbalanced data. The framework was tested on a comprehensive financial dataset, demonstrating the critical role of Gradient Boosting and traditional features like home ownership in improving predictive accuracy. The research illustrates the framework's capacity to augment credit risk assessment, curtail defaults, and facilitate decision-making, providing financial institutions with a robust instrument for operational efficiency and financial stability.
Keywords
References
- Abakarim, Y., Lahby, M., & Attioui, A. (2018). Towards an efficient real-time approach to loan credit approval using deep learning. 9th International Symposium on Signal, Image, Video and Communications, ISIVC 2018 - Proceedings, 306–313. https://doi.org/10.1109/ISIVC.2018.8709173
- Alagic, A., Zivic, N., Kadusic, E., Hamzic, D., Hadzajlic, N., Dizdarevic, M., & Selmanovic, E. (2024). Machine learning for an enhanced credit risk analysis: A comparative study of loan approval prediction models integrating mental health data. Machine Learning and Knowledge Extraction, 6(1), 53–77. https://doi.org/10.3390/make6010004
- Aleksandrova, Y., & Armianova, M. (2022). Evaluation of cost-sensitive machine learning methods for default credit prediction. International Conference Automatics and Informatics, ICAI 2022 - Proceedings, 89–94. https://doi.org/10.1109/ICAI55857.2022.9960023
- Alessi, L., & Savona, R. (2021). Machine learning for financial stability. In Data science for economics and finance: Methodologies and applications (pp. 65–87). https://doi.org/10.1007/978-3-030-66891-4_4
- Antonelli, M., Bernardo, D., Hagras, H., & Marcelloni, F. (2017). Multiobjective evolutionary optimization of type-2 fuzzy rule-based systems for financial data classification. IEEE Transactions on Fuzzy Systems, 25(2), 249–264. https://doi.org/10.1109/TFUZZ.2016.2578341
- Borchani, H., Martínez, A. M., Masegosa, A. R., Langseth, H., Nielsen, T. D., Salmerón, A., Fernández, A., Madsen, A. L., & Sáez, R. (2015). Dynamic Bayesian modeling for risk prediction in credit operations. Frontiers in Artificial Intelligence and Applications, 278, 17–26. https://doi.org/10.3233/978-1-61499-589-0-17
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
- Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13–17 August 2016, 785–794. https://doi.org/10.1145/2939672.2939785
Details
Primary Language
English
Subjects
Finance
Journal Section
Research Article
Authors
Ahmet Akusta
*
0000-0002-5160-3210
Türkiye
Publication Date
April 1, 2026
Submission Date
December 5, 2024
Acceptance Date
February 16, 2025
Published in Issue
Year 2026 Volume: 21 Number: 1