Abstract
The proposed research work aims to develop a novel algorithm to make predictions for various financial institutions to safeguard themselves from fraudsters, and at the same time to ease the pre-sanction process for availing loan and its related verification process. Currently, in the post pandemic world, the proposed algorithm is very essential for the financial institutions, as the rate of loan procurement by individuals has been unprecedently increased and at the same time the chances of loan default has also been increased. For all these cases, first, the bank requires to analyze their Credit Information Bureau India Limited [CIBIL] score and check whether they had done loan repayments within an appropriate time period. Data mining plays a key role to solve such problems and also different algorithms are available in the machine learning domain. Among that, K-nearest neighbor, decision tree, support vector machine, and logistic regression models are taken into consideration for performing data classification with good accuracy. In the present work, the performance of each algorithm is analyzed. The experiments were carried out using python. The accuracy of classifiers will be analyzed by using the following metrics such as Jaccard index, F1-score, and Log loss. This helps to find the best algorithm for classification and the potential of customer, which is much higher than the data mining classification algorithm, and thus it proves to be very helpful for bank officers for sanctioning loan.
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References
Aafer, Y., Du, W., Yin, H.: DroidAPIMiner: mining API-Level features for robust malware detection in android. In: Security and Privacy in Communication Networks, pp 86–103 (2013)
Apilado, V.P., Waner, D.C., Dauten, I.J.: Evaluative techniques in consumer finance—experimental results and policy implications for financial institutions. J. Financ. Quant. Anal. 9(2), 275–283 (1974)
Arun, K., Ishan, G., Sanmeet, K.: Loan approval prediction based on machine learning approch. IOSR J. Comput. Eng. NCRTCSIT 2016, 18–21 (2016)
Boyle, M., Crook, J.N., Hamilton, R., Thomas, L.C.: Methods for credit scaling applied to slow payers. In: Proc. Conf Credit Scoring and Credit Control (eds L. C. Thomas, I. N.Crook and D. B. Edelman), pp. 75–90 (1992)
Chambers, J.M.: Computational Methods for Data Analysis. Applied Statistics, Wiley, 1(2), 1–10 (1977)
Chen, M.C., Huang, S.H.: Credit scoring and rejected instances reassigning through evolutionary computation techniques. Expert Syst. Appl. 24(4), 433–441 (2003)
Hand, D.J., Vinciotti, V.: Choosing k for two-class nearest neighbor classifiers with unbalanced classes. Pattern Recognit. Lett. 24(9–10), 1555–1562 (2003)
Hanumantha Rao, K., Srinivas, G., Damodhar, A., Vikar Krishna, M.: Implementation of anomaly detection technique using machine learning algorithms. Int. J. Comput. Sci. Telecommun. 2(3), 25–30 (2011)
He, Y., Han, J., Zeng, S.: Classification algorithm based on ımproved ID3 in bank loan application. Inf. Eng. Appl. 1124–1130 (2012)
Huang, L., Zhou, C.G., Zhou, Y.-Q., Wang, Z.: Research on data mining algorithms for automotive customers’ behavior prediction problem. In: 2008 Seventh International Conference on Machine Learning and Applications (2008). doi:https://doi.org/10.1109/ICMLA.2008.23
Islam, M.J., Wu, Q.M.J., Ahmadi, M., Sid-Ahmed, M.A.: Investigating the performance of Naive-Bayes classifiers and K- nearest neighbor classifiers. In: International Conference on Convergence Information Technology (ICCIT 2007), pp. 1541–1546 (2007)
Kaishe, Q., Wenli, C., Junhong, W.: The ID3 algorithm an improved algorithm. Comput. Eng. Appl. 39(25), 104–107 (2003)
Keerthi, S., Gilbert, E.: Convergence of a generalized SMO algorithm for SVM classifier design. Mach. Learn. 46, 351–360 (2002)
Li, F.: The hybrid credit scoring strategies based on KNN classifier. In: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, Tianjin, 2009, pp. 330–334 (2009)
Marinakis, Y., Marinaki, M., Doumpos, M., et al.: Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment. J. Global Optim. 42, 279–293 (2008)
Otgler, Y.E.: A credit scoring model for commercial loans. J. Money Credit Bank. 2(4), 435–445 (1970)
Paredes, R., Vidal, E.: A class-dependent weighted dissimilarity measure for nearest neighbor classification problems. Pattern Recogn. Lett. 21(12), 1027–1036 (2000)
Ram, B., Rama Satish, A.: Improved of K-nearest neighbor techniques in credit scoring. Int. J. Dev. Comput. Sci. Technol. 1(2), (2013)
Sahay, B.S., Ranjan, J.: Real time business intelligence in supply chain analytics. Inf. Manage. Comput. Secur. 16(1), 28–48 (2008)
Sutrisno, H., Halim, S.: Credit scoring refinement using optimized logistic regression. In: 2017 International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), Denpasar, pp. 26–31 (2017)
Wei, G., Yingjie, S., Mu, Y.X.: Commercial bank credit risk evaluation method based on decision tree algorithm. In: 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, Nanchang, pp. 285–288 (2015)
White, C.: The role of business intelligence in knowledge management. Bus. Intell. Network. (2005)
Xia, Li.: ID3 classification algorithm application in bank customers erosion. J. Comput. Technol. Dev. 19(3), (2009)
You, H.: A knowledge management approach for real-time business ıntelligence. In: 2nd International Workshop on Intelligent Systems and Applications (2010). doı:https://doi.org/10.1109/IWISA.2010.5473385
Zhang, X., Zhou, Z.: Credit Scoring model based on kernel density estimation and support vector machine for group feature selection. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, pp. 1829–1836 (2018)
Zhu, H., Zhong, Y.: Based on improved ID3 information gain feature selection method. Comput. Eng. 36(8), (2010)
Zou, Y., Fan, C.: Based on the attribute importance ID3 algorithm. Comput. Appl. 28, 145–149 (2008)
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Hemachandran, K., Rodriguez, R.V., Toshniwal, R., Junaid, M., shaw, L. (2022). Performance Analysıs of Different Classıfıcatıon Algorıthms for Bank Loan Sectors. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_16
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