Abstract
Social banking and microfinance have become the key factors for supporting low-income families and underprivileged citizens as well as developing and sponsoring microenterprises. This role has become more important, especially with the widespread of COVID-19 pandemic and its economic repercussions on individuals and microenterprises. We propose in this chapter a data-driven study on the repercussions of the COVID-19 pandemic on the beneficiaries’ characteristics of social and microloans. Our analysis is based on a real case study of Saudi Social Development Bank (SDB) by analyzing microfinance loans granted by this important social financing bank for the years 2019 and 2020. Our study mainly investigates the changes in demographic, social, and economic characteristics of the beneficiaries using both a bivariate exploratory analysis and a decision tree classifier. A machine learning decision tree classification model has been built, for individual and business microcredits, to easily visualize the main beneficiary characteristics of each credit category before and during the COVID-19 pandemic. The built decision trees allowed to deeply understand the main characteristics of each credit category which will help managers to design more suitable and fitted microfinance products.
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References
O. Weber, S. Remer, Social banking – Introduction, in Social Banks and the Future of Sustainable Finance, ed. by O. Weber, S. Remer, (Routledge, London, 2011), pp. 1–14
O. Weber, Social banking: Concept, definitions and practice. Global Soc. Pol. Interdiscipl. J. Publ. Pol. Soc. Dev 14(2), 265–267 (2016). https://doi.org/10.1177/1468018114539864
D.W. Ariani, Social capital moderating roles towards relationship of motives, personality and organizational citizenship behaviour: Cases in Indonesian banking industry. South-East Asian J. Manag 4(2) (2016). https://doi.org/10.21002/seam.v4i2.5637
J.R. Quinlan, Improved use of continuous attributes in C 4.5. J. Artif. Intell. Res. 4, 77–90 (1996)
I. Ibarguren, J.M. Pérez, J. Muguerza, I. Gurrutxaga, O. Arbelaitz, Coverage-based resampling: Building robust consolidated decision trees. Knowl. Base Syst 79, 51–67 (2015). https://doi.org/10.1016/j.knosys.2014.12.023
Weka official documentation. (2021). Available online at: https://weka.sourceforge.io/doc.dev/weka/classifiers/trees/REPTree.html
S. Sathyadevan, R.R. Nair, Comparative analysis of Decision Tree Algorithms: ID3, C4.5 and random forest, in Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol. 31, (Springer, New Delhi, 2015) https://doi.org/10.1007/978-81-322-2205-7_51
J.R. Quinlan, Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
S. Rajesh, A. Chandrasekar, Esteemed software patterns for banking system. Clust. Comput. 22, 11087–11099 (2019) https://doi-org.sdl.idm.oclc.org/10.1007/s10586-017-1304-7
I. Chaabane, R. Guermazi, M. Hammami, Enhancing techniques for learning decision trees from imbalanced data. Adv. Data Anal. Classif 14, 677–745 (2020) https://doi-org.sdl.idm.oclc.org/10.1007/s11634-019-00354-x
D. Saraswat, P. Singh, Comparison of different decision tree algorithms for predicting the heart disease, in Communications in Computer and Information Science, vol. 1241, (Springer, Singapore, 2020). https://doi-org.sdl.idm.oclc.org/10.1007/978-981-15-6318-8_21
M. Teixeira Cazzolato, M. Xavier Ribeiro, C. Yaguinuma, M. Terezinha Prado Santos, A statistical decision tree algorithm for data stream classification, in Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3, (ICEIS, 2013), pp. 217–223. https://doi.org/10.5220/0004447202170223
S. Bhatore, L. Mohan, Y.R. Reddy, Machine learning techniques for credit risk evaluation: A systematic literature review. J. Bank Financ. Technol 4, 111–138 (2020) https://doi-org.sdl.idm.oclc.org/10.1007/s42786-020-00020-3
A.G.C. de Sá, A.C.M. Pereira, G.L. Pappa, A customized classification algorithm for credit card fraud detection. Eng. Appl. Artif. Intell 72, 21–29 (2018). https://doi.org/10.1016/j.engappai.2018.03.011
J.V. Devi, K.S. Kavitha, Fraud detection in credit card transactions by using classification algorithms, in Proceedings of 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), (IEEE, Mysore, INDIA, 2017), pp. 125–131
O. Ozgur, E.T. Karagol, F.C. Ozbugday, Machine learning approach to drivers of bank lending: Evidence from an emerging economy. Finan. Innov. 7(20) (2021) https://doi.org/10.1186/s40854-021-00237-1
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Ncir, CE.B., Alyoubi, B., Alrazyeg, R. (2022). Data-Driven Analysis of Microfinance and Social Loans Before and During the COVID-19 Pandemic Using Exploratory Analysis and Decision Tree Classifiers. In: Alyoubi, B., Ben Ncir, CE., Alharbi, I., Jarboui, A. (eds) Machine Learning and Data Analytics for Solving Business Problems. Unsupervised and Semi-Supervised Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-18483-3_2
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