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

Key Findings & Data: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.

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Conclusion & Impact:This study recommends the use of SVC in banking system while predicting acceptance of bank loan offers.

🔬Design:Research Article

📍Scope:Comprehensive Sample

⚙️Variables:Bank loan approval · Comparison with kernels · Machine learning

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

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Cite This Article

AKÇA, M. F., SEVLİ, O. (2022). Predicting acceptance of the bank loan offers by using support vector machines. International Advanced Researches and Engineering Journal, Cilt: 6 - Sayı: 2, 142–147. https://ajindex.com/articles/539257

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References (13)

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
Bhandari, M., How to predict loan eligibility using machine learning models. [cited 2022 02 January]; Available from:
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
Walke, K. Bank personal loan modelling. [cited 2021 03 October]; Available from:
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
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
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
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
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
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
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
Xu, J., Z. Lu, and Y. Xie, Loan default prediction of Chinese P2P market: a machine learning methodology. Scientific Reports, 2021. 11: 1-19
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.