Utilization of Data Mining to Predict Non-Performing Loan

Volume 5, Issue 4, Page No 252-256, 2020

Author’s Name: Yosaphat Catur Widiyono^1,a), Sani Muhamad Isa²

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¹Computer Science Department, Binus Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta 11480, Indonesia
²Computer Science Department, Binus Online Learning, Bina Nusantara University, Jakarta 11480, Indonesia

^a)Author to whom correspondence should be addressed. E-mail: yosaphat.widiyono@binus.ac.id

Adv. Sci. Technol. Eng. Syst. J. 5(4), 252-256 (2020); DOI: 10.25046/aj050431

Keywords: Data Mining, Non-Performing Loan, Machine Learning

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Abstract

In the banking industry, the existence of problem loans is inevitable. NPL (Non-Performing Loan) will certainly have an impact on the reduction in the capital of a bank. One good step in reducing the risk of credit default or the emergence of non-performing loans is to take proper care of debtors who begin to experience payment constraints. The main obstacle experienced in bank management, especially in the credit sector, is being unable to identify or detect potential debtors early due to a large amount of data and manual processing. In this study, the debtor payment history is presented as data to predict the existence of problem loans. History payment can be used to predict bad loans. The technic of data mining in this experiment is a new method. The results of research conducted using Naïve Bayes, Decision Tree, K-NN, Rule Induction, Logistic Regression, Random Forest, Generalized Linear Model, and Gradient Boosted Trees as a comparison then choose the method that has the highest accuracy to be implemented in making additional modules on the core banking system. Random Forest is the model that has the highest accuracy of 96.55%.

Received: 18 June 2020, Accepted: 09 July 2020, Published Online: 28 July 2020

References (17)

M. B. Alexandri and T. I. Santoso, “Non Performing Loan: Impact of Internal and External Factor (Evidence in Indonesia),” Int. J. Humanit. Soc. Sci. Invent., 4(1), 87–91, 2015.
R. Agustiningrum, “Analisis Pengaruh Car, Npl, Dan Ldr Terhadap Profitabilitas Pada Perusahaan Perbankan,” E-Jurnal Manaj. Univ. Udayana, 2(8), 255030, 2013.
H. Elaidi, Z. Benabbou, and H. Abbar, “A comparative study of algorithms constructing decision trees: Id3 and c4.5,” ACM Int. Conf. Proceeding Ser., 3–7, 2018. doi: 10.1145/3230905.3230916.
J. S. Gil, A. J. P. Delima, and R. N. Vilchez, “Predicting students’ dropout indicators in public school using data mining approaches,” Int. J. Adv. Trends Comput. Sci. Eng., 9(1), 774–778, 2020. doi: 10.30534/ijatcse/2020/110912020.
X. Wang, C. Zhou, and X. Xu, “Application of C4.5 decision tree for scholarship evaluations,” Procedia Comput. Sci., 151( 2018) 179–184, 2019. doi: 10.1016/j.procs.2019.04.027.
W. Puarungroj, N. Boonsirisumpun, P. Pongpatrakant, and S. Phromkhot, “Application of data mining techniques for predicting student success in English exit exam,” ACM Int. Conf. Proceeding Ser., 1–6, 2018. doi: 10.1145/3164541.3164638.
C. J. Mantas, J. Abellán, and J. G. Castellano, “Analysis of Credal-C4.5 for classification in noisy domains,” Expert Syst. Appl., 2016. doi: 10.1016/j.eswa.2016.05.035.
H. Byeon, “A Prediction Model for Mild Cognitive Impairment Using Random Forests,” Int. J. Adv. Comput. Sci. Appl., 6(12), 8–12, 2015. doi: 10.14569/ijacsa.2015.061202.
A. Shamshur and L. Weill, “Does bank efficiency influence the cost of credit?,” J. Bank. Financ., 2019. doi: 10.1016/j.jbankfin.2019.05.002.
X. Ye, L. an Dong, and D. Ma, “Loan evaluation in P2P lending based on Random Forest optimized by genetic algorithm with profit score,” Electron. Commer. Res. Appl., 2018. doi: 10.1016/j.elerap.2018.10.004.
S. Moradi and F. Mokhatab Rafiei, “A dynamic credit risk assessment model with data mining techniques: evidence from Iranian banks,” Financ. Innov., 5(1), 2019. doi: 10.1186/s40854-019-0121-9.
I. G. N. N. Mandala, C. B. Nawangpalupi, and F. R. Praktikto, “Assessing Credit Risk: An Application of Data Mining in a Rural Bank,” Procedia Econ. Financ., 4, Icsmed, 406–412, 2012. doi: 10.1016/s2212-5671(12)00355-3.
O. R. Devi, “International Journal of Advanced Trends in Computer Science and Engineering Available Online at http://www.warse.org/ijatcse/static/pdf/file/ijatcse02422015.pdf,” 4(2), 15–21, 2015.
N. Sun, B. Sun, J. (Denny) Lin, and M. Y. C. Wu, “Lossless Pruned Naive Bayes for Big Data Classifications,” Big Data Res., 2018. doi: 10.1016/j.bdr.2018.05.007.
A. Singh, M. N., and R. Lakshmiganthan, “Impact of Different Data Types on Classifier Performance of Random Forest, Naïve Bayes, and K-Nearest Neighbors Algorithms,” Int. J. Adv. Comput. Sci. Appl., 8(12), 1–10, 2017, doi: 10.14569/ijacsa.2017.081201.
M. Hoechstoetter, A. Nazemi, and S. T. Rachev, “Recovery Rate Modelling of Non-performing Consumer Credit Using Data Mining Algorithms,” 12, 2012.
N. S. Buot, “Multiple intelligences and reading comprehension of senior high school students: A response evaluation through educational data mining technique,” Int. J. Adv. Trends Comput. Sci. Eng., 8(6), 2871–2876, 2019, doi: 10.30534/ijatcse/2019/30862019.

Utilization of Data Mining to Predict Non-Performing Loan