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

doi:doi:10.11648/j.ijdsa.20261203.11

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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

Published in International Journal of Data Science and Analysis (Volume 12, Issue 3)

Received: 20 May 2026 Accepted: 30 May 2026 Published: 3 July 2026

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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.

Published in	International Journal of Data Science and Analysis (Volume 12, Issue 3)
DOI	10.11648/j.ijdsa.20261203.11
Page(s)	37-51
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Loan Eligibility, Machine Learning, XGBoost, Support Vector Machine, Hybrid Model

References

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[7]	Davinder Paul Singh et al. "Predictive Modeling for Bank Loan Approval: From Data to Decisions". In: Procedia Computer Science 259 (2025), pp. 1426-1431. https://doi.org/10.1016/j.procs.2025.04.097
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[14]	Xu Zhu et al. "Explainable prediction of loan default based on machine learning models". In: Data Science and Management 6.3 (2023), pp. https://doi.org/10.1016/j.dsm.2023.04.003
[15]	Malti Bansal, Apoorva Goyal, and Apoorva Choudhary. "A comparative analysis of K-nearest neighbor, genetic, support vector machine, decision tree, and long short term memory algorithms in machine learning". In: Decision analytics journal 3 (2022), p. 100071. https://doi.org/10.1016/j.dajour.2022.100071
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APA Style

Muhoro, J. G., Gitonga, H. M., Ngure, J. N. (2026). Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting. International Journal of Data Science and Analysis, 12(3), 37-51. https://doi.org/10.11648/j.ijdsa.20261203.11

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ACS Style

Muhoro, J. G.; Gitonga, H. M.; Ngure, J. N. Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting. Int. J. Data Sci. Anal. 2026, 12(3), 37-51. doi: 10.11648/j.ijdsa.20261203.11

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AMA Style

Muhoro JG, Gitonga HM, Ngure JN. Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting. Int J Data Sci Anal. 2026;12(3):37-51. doi: 10.11648/j.ijdsa.20261203.11

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@article{10.11648/j.ijdsa.20261203.11,
  author = {James Githaiga Muhoro and Harun Mwangi Gitonga and Josephine Njeri Ngure},
  title = {Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting},
  journal = {International Journal of Data Science and Analysis},
  volume = {12},
  number = {3},
  pages = {37-51},
  doi = {10.11648/j.ijdsa.20261203.11},
  url = {https://doi.org/10.11648/j.ijdsa.20261203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20261203.11},
  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.},
 year = {2026}
}

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TY  - JOUR
T1  - Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting
AU  - James Githaiga Muhoro
AU  - Harun Mwangi Gitonga
AU  - Josephine Njeri Ngure
Y1  - 2026/07/03
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DO  - 10.11648/j.ijdsa.20261203.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 37
EP  - 51
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20261203.11
AB  - 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.
VL  - 12
IS  - 3
ER  -

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Author Information

James Githaiga Muhoro

School of Pure and Applied Sciences, Kirinyaga University, Kerugoya, Kenya

Contact Email
Harun Mwangi Gitonga

School of Pure and Applied Sciences, Kirinyaga University, Kerugoya, Kenya
Josephine Njeri Ngure

School of Pure and Applied Sciences, Kirinyaga University, Kerugoya, Kenya

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APA Style

Muhoro, J. G., Gitonga, H. M., Ngure, J. N. (2026). Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting. International Journal of Data Science and Analysis, 12(3), 37-51. https://doi.org/10.11648/j.ijdsa.20261203.11

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ACS Style

Muhoro, J. G.; Gitonga, H. M.; Ngure, J. N. Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting. Int. J. Data Sci. Anal. 2026, 12(3), 37-51. doi: 10.11648/j.ijdsa.20261203.11

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AMA Style

Muhoro JG, Gitonga HM, Ngure JN. Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting. Int J Data Sci Anal. 2026;12(3):37-51. doi: 10.11648/j.ijdsa.20261203.11

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@article{10.11648/j.ijdsa.20261203.11,
  author = {James Githaiga Muhoro and Harun Mwangi Gitonga and Josephine Njeri Ngure},
  title = {Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting},
  journal = {International Journal of Data Science and Analysis},
  volume = {12},
  number = {3},
  pages = {37-51},
  doi = {10.11648/j.ijdsa.20261203.11},
  url = {https://doi.org/10.11648/j.ijdsa.20261203.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20261203.11},
  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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Determining Loan Eligibility in the Banking Sector Using a Hybrid Model of Support Vector Machine and Extreme Gradient Boosting
AU  - James Githaiga Muhoro
AU  - Harun Mwangi Gitonga
AU  - Josephine Njeri Ngure
Y1  - 2026/07/03
PY  - 2026
N1  - https://doi.org/10.11648/j.ijdsa.20261203.11
DO  - 10.11648/j.ijdsa.20261203.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 37
EP  - 51
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20261203.11
AB  - 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.
VL  - 12
IS  - 3
ER  -

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