Research Article
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Year 2022, Volume: 6 Issue: 2, 142 - 147, 15.08.2022

Mehmet Furkan Akça Onur Sevli

https://doi.org/10.35860/iarej.1058724

Abstract

References

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  • 4. Walke, K. Bank personal loan modelling. [cited 2021 03 October]; Available from: https://www.kaggle.com/krantiswalke/bank-personal-loan-modelling.
  • 5. Tejaswini, J., T.M. Kavya, R.D.N. Ramya, P.S. Triveni, and V.R. Maddumala, Accurate loan approval prediction based on machine learning approach. Journal of Engineering Sciences, 2020. 11(4): p. 523-532.
  • 6. Pandey, N., R. Gupta, S. Uniyal, and V. Kumar, Loan approval prediction using machine learning algorithms approach. International Journal of Innovative Research in Technology, 2021. 8(1): p. 898-902.
  • 7. Boser, B.E., I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the fifth annual workshop on Computational learning theory, 1992. Association for Computing Machinery: Pittsburgh, Pennsylvania, USA: p. 144–152.
  • 8. Auria, L., and R.A. Moro, Support vector machines (SVM) as a technique for solvency analysis. DIW Berlin Discus. Paper, 2008. [cited 2022 02 January] Available from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1424949.
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  • 10. Tian, Y., Y. Shi, and X. Liu, Recent advances on support vector machines research. Technological and Economic Development of Economy, 2012. 18(1): p. 5-33.
  • 11. Dall'Asta Rigo, E.Y., Evaluation of stacking for Predicting credit risk scores. MSc Thesis at TED University Graduate School Applied Data Science, 2020. p. 1-75.
  • 12. Xu, J., Z. Lu, and Y. Xie, Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports, 2021. 11: 1-19.
  • 13. Huang, Z., H. Chen, C.J. Hsu, W.H. Chen, and S. Wu, Credit rating analysis with support vector machines and neural networks: A market comparative study. Decis. Support Syst., 2004. 37: 543-558.
  • 14. Kadam, A.S., S.R. Nikam, A.A. Aher, G.V. Shelke, and A.S. Chandgude, Prediction for loan approval using machine learning algorithm. International Research Journal of Engineering and Technology (IRJET), 2021. 8(4): 4089-4092.
  • 15. Bayraktar, M., M.S. Aktas, O. Kalipsiz, O. Susuz, and S. Bayraci, Credit risk analysis with classification restricted boltzmann machine. in Proceeding of 26th Signal Processing and Communications Applications Conference (SIU), 2018, p. 1-4.
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  • 21. Sheikh, M.A., A.K. Goel, and T. Kumar. An Approach for Prediction of Loan Approval using Machine Learning Algorithm. in Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020), IEEE Xplore Part Number: CFP20V66-ART, 2020. p. 490-494.
  • 22. Vimala, S., and K. Sharmili. Prediction of loan risk using naive bayes and support vector machine. in International Conference on Advancements in Computing Technologies - ICACT 2018. International Journal on Future Revolution in Computer Science & Communication Engineering (Special Issue), 2018. 4(2): p. 110-113. Available from: http://www.ijfrcsce.org/index.php/ijfrcsce/Special_Issue/ICACT_2018_Track.
  • 23. Fati, S.M., Machine learning-based prediction model for loan status approval. Journal of Hunan University Natural Sciences, 2021. 48(10): p. 1-8.
  • 24. Madaan, M., A. Kumar, C. Keshri, R. Jain, and P. Nagrath, Loan default prediction using decision trees and random forest: A comparative study. in IOP Conference Series: Materials Science and Engineering. 2021. IOP Publishing, p.1-12.
  • 25. Sreesouthry, S., A. Ayubkhan, M.M. Rizwan, D. Lokesh, and K.P. Raj, Loan Prediction Using Logistic Regression in Machine Learning. Annals of the Romanian Society for Cell Biology, 2021. 25(4): p. 2790-2794.
  • 26. Yaurita, F., and Z. Rustam. Application of support vector machines for reject inference in credit scoring. in Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017 (ISCPMS2017), AIP Conf. Proc. 2023, 020209-1-020209-6; https://doi.org/10.1063/1.5064206.
  • 27. Kumar, R., et al., Prediction of loan approval using machine learning. International Journal of Advanced Science and Technology, 2019. 28(7): p. 455 - 460.
  • 28. Ndayisenga, T., Bank loan approval prediction using machine learning techniques. MSc Dissertation in Data Science in Actuarial Science at the African Center of Excellence in Data Science in University of Rwanda, 2021. p. 1-28.
  • 29. Maroco, J., D. Silva, A. Rodrigues, M. Guerreiro, I. Santana, and A. de Mendonca, Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC research notes, 2011. 4(299): p. 1-14.
  • 30. Guenther, N., and M. Schonlau, Support vector machines. The Stata Journal, 2016. 16(4): p. 917-937.
  • 31. Prasad, K.G.S., P.V.S. Chidvilas, and V.V. Kumar, Customer loan approval classification by supervised learning model. International Journal of Recent Technology and Engineering, 2019. 8(4): p. 9898-9901.

Predicting acceptance of the bank loan offers by using support vector machines

Year 2022, Volume: 6 Issue: 2, 142 - 147, 15.08.2022

Mehmet Furkan Akça Onur Sevli

https://doi.org/10.35860/iarej.1058724

Abstract

Loans are one of the main profit sources in banking system. Banks try to select reliable customers and offer them personal loans, but customers can sometimes reject bank loan offers. Prediction of this problem is an extra work for banks, but if they can predict which customers will accept personal loan offers, they can make a better profit. Therefore, at this point, the aim of this study is to predict acceptance of the bank loan offers using the Support Vector Machine (SVM) algorithm. In this context, SVM was used to predict results with four kernels of SVM, with a grid search algorithm for better prediction and cross validation for much more reliable results. Research findings show that the best results were obtained with a poly kernel as 97.2% accuracy and the lowest success rate with a sigmoid kernel as 83.3% accuracy. Some precision and recall values are lower than normal ones, like 0.108 and 0.008 due to unbalanced dataset, like for 1 true value, there are 9 negative values (9.6% true value). This study recommends the use of SVC in banking system while predicting acceptance of bank loan offers.

Keywords

Bank loan approval Comparison with kernels Machine learning Support vector machines

References

  • 1. Arun, K., G. Ishan, and K. Sanmeet, Loan approval prediction based on machine learning approach. IOSR J. Comput. Eng, 2016. 18(3): p. 18-21.
  • 2. Bhandari, M., How to predict loan eligibility using machine learning models. [cited 2022 02 January]; Available from: https://towardsdatascience.com/predict-loan-eligibility-using-machine-learning-models-7a14ef904057.
  • 3. Aphale, A.S., and S.R. Shinde, Predict loan approval in banking system machine learning approach for cooperative banks loan approval. International Journal of Engineering Research & Technology, 2020. 9(8): 991-995.
  • 4. Walke, K. Bank personal loan modelling. [cited 2021 03 October]; Available from: https://www.kaggle.com/krantiswalke/bank-personal-loan-modelling.
  • 5. Tejaswini, J., T.M. Kavya, R.D.N. Ramya, P.S. Triveni, and V.R. Maddumala, Accurate loan approval prediction based on machine learning approach. Journal of Engineering Sciences, 2020. 11(4): p. 523-532.
  • 6. Pandey, N., R. Gupta, S. Uniyal, and V. Kumar, Loan approval prediction using machine learning algorithms approach. International Journal of Innovative Research in Technology, 2021. 8(1): p. 898-902.
  • 7. Boser, B.E., I.M. Guyon, and V.N. Vapnik, A training algorithm for optimal margin classifiers, in Proceedings of the fifth annual workshop on Computational learning theory, 1992. Association for Computing Machinery: Pittsburgh, Pennsylvania, USA: p. 144–152.
  • 8. Auria, L., and R.A. Moro, Support vector machines (SVM) as a technique for solvency analysis. DIW Berlin Discus. Paper, 2008. [cited 2022 02 January] Available from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1424949.
  • 9. Li, J., J. Liu, W. Xu, and Y. Shi, Support vector machines approach to credit assessment, in Computational Science-ICCS 2004, Lecture Notes in Computer Science 3039, Berlin Heidelberg: Springer. p. 892-899.
  • 10. Tian, Y., Y. Shi, and X. Liu, Recent advances on support vector machines research. Technological and Economic Development of Economy, 2012. 18(1): p. 5-33.
  • 11. Dall'Asta Rigo, E.Y., Evaluation of stacking for Predicting credit risk scores. MSc Thesis at TED University Graduate School Applied Data Science, 2020. p. 1-75.
  • 12. Xu, J., Z. Lu, and Y. Xie, Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports, 2021. 11: 1-19.
  • 13. Huang, Z., H. Chen, C.J. Hsu, W.H. Chen, and S. Wu, Credit rating analysis with support vector machines and neural networks: A market comparative study. Decis. Support Syst., 2004. 37: 543-558.
  • 14. Kadam, A.S., S.R. Nikam, A.A. Aher, G.V. Shelke, and A.S. Chandgude, Prediction for loan approval using machine learning algorithm. International Research Journal of Engineering and Technology (IRJET), 2021. 8(4): 4089-4092.
  • 15. Bayraktar, M., M.S. Aktas, O. Kalipsiz, O. Susuz, and S. Bayraci, Credit risk analysis with classification restricted boltzmann machine. in Proceeding of 26th Signal Processing and Communications Applications Conference (SIU), 2018, p. 1-4.
  • 16. IBM. [cited 2022 02 January]; Available from: https://www.ibm.com/cloud/learn/supervised-learning.
  • 17. Ray, S. Understanding support vector machine (SVM) algorithm from examples (along with code). September 13, 2017 [cited 2022 02 January]; Available from: https://www.analyticsvidhya.com/blog/2017/09/understaing-support-vector-machine-example-code/.
  • 18. Gandhi, R. Support vector machine-Introduction to machine learning algorithms. [cited 2022 02 January]; Available from: https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47.
  • 19. Hsu, C.W., C.C. Chang, and C.J. Lin, A practical guide to support vector classification. Technical Report 2003, Department of Computer Science and Information Engineering, University of National Taiwan. p. 1-12.
  • 20. Srivastava, T, 11 important model evaluation metrics for machine learning everyone should know. August 6, 2019 [cited 2022 02 January]; Available from: https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/.
  • 21. Sheikh, M.A., A.K. Goel, and T. Kumar. An Approach for Prediction of Loan Approval using Machine Learning Algorithm. in Proceedings of the International Conference on Electronics and Sustainable Communication Systems (ICESC 2020), IEEE Xplore Part Number: CFP20V66-ART, 2020. p. 490-494.
  • 22. Vimala, S., and K. Sharmili. Prediction of loan risk using naive bayes and support vector machine. in International Conference on Advancements in Computing Technologies - ICACT 2018. International Journal on Future Revolution in Computer Science & Communication Engineering (Special Issue), 2018. 4(2): p. 110-113. Available from: http://www.ijfrcsce.org/index.php/ijfrcsce/Special_Issue/ICACT_2018_Track.
  • 23. Fati, S.M., Machine learning-based prediction model for loan status approval. Journal of Hunan University Natural Sciences, 2021. 48(10): p. 1-8.
  • 24. Madaan, M., A. Kumar, C. Keshri, R. Jain, and P. Nagrath, Loan default prediction using decision trees and random forest: A comparative study. in IOP Conference Series: Materials Science and Engineering. 2021. IOP Publishing, p.1-12.
  • 25. Sreesouthry, S., A. Ayubkhan, M.M. Rizwan, D. Lokesh, and K.P. Raj, Loan Prediction Using Logistic Regression in Machine Learning. Annals of the Romanian Society for Cell Biology, 2021. 25(4): p. 2790-2794.
  • 26. Yaurita, F., and Z. Rustam. Application of support vector machines for reject inference in credit scoring. in Proceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017 (ISCPMS2017), AIP Conf. Proc. 2023, 020209-1-020209-6; https://doi.org/10.1063/1.5064206.
  • 27. Kumar, R., et al., Prediction of loan approval using machine learning. International Journal of Advanced Science and Technology, 2019. 28(7): p. 455 - 460.
  • 28. Ndayisenga, T., Bank loan approval prediction using machine learning techniques. MSc Dissertation in Data Science in Actuarial Science at the African Center of Excellence in Data Science in University of Rwanda, 2021. p. 1-28.
  • 29. Maroco, J., D. Silva, A. Rodrigues, M. Guerreiro, I. Santana, and A. de Mendonca, Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC research notes, 2011. 4(299): p. 1-14.
  • 30. Guenther, N., and M. Schonlau, Support vector machines. The Stata Journal, 2016. 16(4): p. 917-937.
  • 31. Prasad, K.G.S., P.V.S. Chidvilas, and V.V. Kumar, Customer loan approval classification by supervised learning model. International Journal of Recent Technology and Engineering, 2019. 8(4): p. 9898-9901.
There are 31 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Software Engineering, Engineering
Journal Section Research Articles
Authors

Mehmet Furkan Akça 0000-0003-0289-9606

Onur Sevli 0000-0002-8933-8395

Publication Date August 15, 2022
Submission Date January 16, 2022
Acceptance Date June 13, 2022
Published in Issue Year 2022 Volume: 6 Issue: 2

Cite

APA Akça, M. F., & Sevli, O. (2022). Predicting acceptance of the bank loan offers by using support vector machines. International Advanced Researches and Engineering Journal, 6(2), 142-147. https://doi.org/10.35860/iarej.1058724
AMA Akça MF, Sevli O. Predicting acceptance of the bank loan offers by using support vector machines. Int. Adv. Res. Eng. J. August 2022;6(2):142-147. doi:10.35860/iarej.1058724
Chicago Akça, Mehmet Furkan, and Onur Sevli. “Predicting Acceptance of the Bank Loan Offers by Using Support Vector Machines”. International Advanced Researches and Engineering Journal 6, no. 2 (August 2022): 142-47. https://doi.org/10.35860/iarej.1058724.
EndNote Akça MF, Sevli O (August 1, 2022) Predicting acceptance of the bank loan offers by using support vector machines. International Advanced Researches and Engineering Journal 6 2 142–147.
IEEE M. F. Akça and O. Sevli, “Predicting acceptance of the bank loan offers by using support vector machines”, Int. Adv. Res. Eng. J., vol. 6, no. 2, pp. 142–147, 2022, doi: 10.35860/iarej.1058724.
ISNAD Akça, Mehmet Furkan - Sevli, Onur. “Predicting Acceptance of the Bank Loan Offers by Using Support Vector Machines”. International Advanced Researches and Engineering Journal 6/2 (August 2022), 142-147. https://doi.org/10.35860/iarej.1058724.
JAMA Akça MF, Sevli O. Predicting acceptance of the bank loan offers by using support vector machines. Int. Adv. Res. Eng. J. 2022;6:142–147.
MLA Akça, Mehmet Furkan and Onur Sevli. “Predicting Acceptance of the Bank Loan Offers by Using Support Vector Machines”. International Advanced Researches and Engineering Journal, vol. 6, no. 2, 2022, pp. 142-7, doi:10.35860/iarej.1058724.
Vancouver Akça MF, Sevli O. Predicting acceptance of the bank loan offers by using support vector machines. Int. Adv. Res. Eng. J. 2022;6(2):142-7.
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