Research Article

Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework

Volume: 21 Number: 1 April 1, 2026
EN TR

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

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  8. 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

Publication Date

April 1, 2026

Submission Date

December 5, 2024

Acceptance Date

February 16, 2025

Published in Issue

Year 2026 Volume: 21 Number: 1

APA
Akusta, A. (2026). Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, 21(1), 258-278. https://doi.org/10.17153/oguiibf.1596734
AMA
1.Akusta A. Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2026;21(1):258-278. doi:10.17153/oguiibf.1596734
Chicago
Akusta, Ahmet. 2026. “Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework”. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi 21 (1): 258-78. https://doi.org/10.17153/oguiibf.1596734.
EndNote
Akusta A (April 1, 2026) Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi 21 1 258–278.
IEEE
[1]A. Akusta, “Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework”, Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, vol. 21, no. 1, pp. 258–278, Apr. 2026, doi: 10.17153/oguiibf.1596734.
ISNAD
Akusta, Ahmet. “Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework”. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi 21/1 (April 1, 2026): 258-278. https://doi.org/10.17153/oguiibf.1596734.
JAMA
1.Akusta A. Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2026;21:258–278.
MLA
Akusta, Ahmet. “Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework”. Eskişehir Osmangazi Üniversitesi İktisadi Ve İdari Bilimler Dergisi, vol. 21, no. 1, Apr. 2026, pp. 258-7, doi:10.17153/oguiibf.1596734.
Vancouver
1.Ahmet Akusta. Enhanced Loan Approval Prediction Using a Cascaded Machine Learning Framework. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 2026 Apr. 1;21(1):258-7. doi:10.17153/oguiibf.1596734