Research Article
Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting
James Githaiga Muhoro*,
Harun Mwangi Gitonga,
Josephine Njeri Ngure
Issue:
Volume 12, Issue 3, June 2026
Pages:
37-51
Received:
20 May 2026
Accepted:
30 May 2026
Published:
3 July 2026
DOI:
10.11648/j.ijdsa.20261203.11
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Abstract: The loan prediction models are ever-changing due to changes in technology, whereby financial institutions are adopting various technologies to automate the loan process. The surge of loan applicants with diverse attributes has accelerated the need for machine learning models which can incorporate different applicant attributes and improve accuracy in determining loan eligibility. While the use of individual machine learning models was more robust and accurate, these models have some limitations that may hinder the achievement of optimal results when establishing loan eligibility. Thus, the need to combine two or more individual models and leverage their strengths to improve accuracy and robustness. This study aimed to develop an eXtreme Gradient Boosting (XGBoost)-Support Vector Machine (SVM) hybrid model to determine loan eligibility in the banking sector. This work utilized secondary financial dataset from Google Kaggle. The dataset was preprocessed, transformed and used to train and test the models. Evaluation metrics, namely accuracy, precision, F1-score, recall and Receiver Operating Characteristics-Area Under Curve (ROC-AUC), were used to evaluate the performance and reliability of the XGBoost, SVM and the XGBoost-SVM hybrid model. The XGBoost-SVM hybrid model posted strong performance metrics results with accuracy of 0.78, precision of 0.27, a balanced recall of 0.49, F1-Score of 0.34 and AUC-ROC curve of 0.74. It was therefore evident that the hybrid model was able to leverage the standalone model's strength for better performance in determining loan eligibility.
Abstract: The loan prediction models are ever-changing due to changes in technology, whereby financial institutions are adopting various technologies to automate the loan process. The surge of loan applicants with diverse attributes has accelerated the need for machine learning models which can incorporate different applicant attributes and improve accuracy ...
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