Examining determinants of loan default: An empirical analysis on credit factors in Thai savings and credit cooperatives
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DOIhttp://dx.doi.org/10.21511/imfi.21(4).2024.26
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Article InfoVolume 21 2024, Issue #4, pp. 323-332
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Savings and credit cooperatives (SACCOs) are crucial institutions in promoting financial accessibility. SACCOs provide financial loans to individuals who may not have access to traditional banking. SACCOs take their own risk to get loan defaults from the offerings because member loans are approved without checking the members’ credit background by SACCO committees. This study aims to investigate factors influencing loan defaults of savings and credit cooperatives in Thailand. Based on the savings and credits cooperative database in November 2023, the cooperative has emergency loans, regular loans, and special loans totaling 11,441 contracts. In this study, all loan contracts of this cooperative were used to analyze. The data were divided into two categories of debt classification, including (1) non-default status and (2) default status. The data were analyzed using logistics regression to select the highest accuracy model. Furthermore, the finding reveals that the highest accuracy model, at 99.78%, contains five variables, including interest rate, collateral value, remaining contract duration, outstanding debt, and installment amount. The savings and credit cooperatives institution should adjust the loan interest rates according to economic conditions. Moreover, closely monitoring members with high remaining debt would help the institution prevent loan defaults, and the institution should also create a conservative loan approval policy to reduce its loan default.
Acknowledgments
The research for the work featured in this article is funded by the Prince of Songkla Savings and Credit Cooperatives, Limited.
- Keywords
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JEL Classification (Paper profile tab)G17, G23, G51
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References38
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Tables4
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Figures0
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- Table 1. Sample loan data from the savings and credit cooperatives, divided by 80% and 20% proportions, by variable
- Table 2. Correlation coefficients of predictive variables
- Table 3. Results of logistic regression analysis of variables leading to loan default
- Table 4. Result validation
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- Abdou, H., Abdallah, W., Mulkeen, J., Ntim, C., & Wang, Y. (2017). Prediction of financial strength ratings using machine learning and conventional techniques. Investment Management and Financial Innovations, 14(4), 194-211.
- Ali Albastaki, A. (2022). Loan Default Prediction System. Thesis. Rochester Institute of Technology.
- Antar, M. (2024). Evaluating the Impact of Borrower Characteristics, Loan Specific Parameters, and Property Conditions on Mortgage Default Risk. Theoretical and Practical Research in Economic Fields, 15(2), 481-501.
- Assawawongsathien, S., Srichart, K., & Nudam, R. (2017). The role and risks of the savings cooperative system. Puey Ungphakorn Institute for Economic Research. Thailand: Bangkok.
- Barua, S., Gavandi, D., Sangle, P., Shinde, L., & Ramteke, J. (2021). Swindle: Predicting the Probability of Loan Defaults using CatBoost Algorithm. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1710-1715). Erode, India.
- Boateng, E., & Abaye, D. (2019). A Review of the Logistic Regression Model with Emphasis on Medical Research. Journal of Data Analysis and Information Processing, 11(4), 190-207.
- Boughaci, D., & Alkhawaldeh, A. A. (2019). A cooperative classification system for credit scoring. Smart Technologies and Innovation for a Sustainable Future. Proceedings of the 1st American University in the Emirates International Research Conference (pp. 11-20). Dubai, UAE.
- Bravo, C., Thomas, L. C., & Weber, R. (2015). Improving credit scoring by differentiating defaulter behavior. Journal of the Operational Research Society, 66(5), 771-781.
- Chushim, K. (2013). Factors causing the non-performing loans on housing-loan of a commercial bank. Thammasat University.
- Cooperative Auditing Department. (2016). The Registrar of Cooperatives Regulations on the Classification of Loan Quality and the Provision for Doubtful Debts.
- Cooperative Promotion Department. (2021). Background.
- El Hancha Sfar, F., & Ben Ouda, O. (2016). Contribution of Cooperative Banks to the Regional Economic Growth: Empirical Evidence from France. International Journal of Economics and Financial Issues, 6(2), 508-514.
- Galor, Z. (1999). Credit and Saving Cooperatives: A new conceptual approach.
- Hammond, P., Opoku, M., Kwakwa, P., & Amissah, E. (2023) Predictive models of going concerns and business failure. Cogent Business & Management, 10(2), 1-24.
- Imsuk, S. (2018). Factors affecting the credit default for the loan of the land bank administration institute (public organization). Thamasat University.
- Kamphod, K. (2019). Factors Affecting the Default on Student Loan Fund (SLF) in Thailand. Chaing Mai University.
- Kaplan, I., & Mccay, B. (2004). Cooperative research, co-management and the social dimension of fisheries science and management. Marine Policy, 28, 257-258.
- Keawmanee, S. (2016). The Role of Cooperatives in Economic and Social Development by Members of Thanto Agricultural Cooperatives Limited, Yala Province. Bangkok: Sukhothai Thammathirat Open University.
- Laliwan, S., & Potipiroon, W. (2022). Board Capital, Organizational Capital and Organizational Performance of Agricultural and Non-agricultural Co-operatives in Thailand. ABAC Journal, 42(2), 195-215.
- Lokesha, & Hawaldar, I. (2019). Impact of factors on the utilization of agricultural credit of banks: an analysis from the borrowers’ perspective. Banks and Bank Systems, 14(1), 181-192.
- Maichandrang, S. (2021). An Analysis of debt default probability of bank client: A Case study of teacher and local government of Krung Thai Bank, Hod Branch. Chaing Mai University.
- McKillop, D., French, D., Quinn, B., Sobiech, A. L., & Wilson, J. S. (2020). Cooperative financial institutions: A review of the literature. International Review of Financial Analysis, 71, 1-11.
- Namwong, S., & Janyasuprab, P. (2018). The Roles of Thai Cooperatives in New Economic Age. Walailak Journal of Social Science, 11(1), 143-183.
- Nguyen, H. T. (2015). How is credit scoring used to predict default in China? (EconomiX Working Papers 2015-1). University of Paris Nanterre, EconomiX.
- Notananda, V. (2000). Factors causing defaults on housing-loan repayment of a commercial bank in Muang district, Chiang Mai Province. Chiang Mai University.
- Omeke, M., Ngoboka, P., Nkote, I., & Kayongo, I. (2019) The relationship between complexity behavior and enterprise growth: A case of savings and credit cooperatives in Uganda. Cogent Business & Management, 6(1), 1-15.
- Onay, C., & Öztürk, E. (2018). A review of credit scoring research in the age of Big Data. Journal of Financial Regulation and Compliance, 26(3), 382-405.
- Pattarapornpairot, N. (2020). Examining the relationship between macroeconomic variables on nonperforming loans from commercial banks in Thailand. Thamasat University.
- Petrides, G., Moldovan, D., Coenen, L., Guns, T., & Verbeke, W. (2022). Cost-sensitive learning for profit-driven credit scoring. Journal of the Operational Research Society, 73(2), 338-350.
- Saha, A., Hock Eam, L., & Goh Yeok, S. (2023). Housing loan default in Malaysia: an analytical insight and policy implications. International Journal of Housing Markets and Analysis, 16(2), 273-291.
- Sariannidis, N., Papadakis, S., Garefalakis, A., Lemonakis, C., & Kyriaki-Argyro, T. (2020). Default avoidance on credit card portfolios using accounting, demographical and exploratory factors: decision making based on machine learning (ML) techniques. Annals of Operations Research, 294, 715-739.
- Steenackers, A., & Goovaerts, M. J. (1989). A credit scoring model for personal loans. Insurance: Mathematics and Economics, 8(1), 31-34.
- The Savings and Credit Cooperatives. (2023). Annual report 2023. Songkhla: The Prince of Songkla University Savings and Credits Cooperative, Limited.
- Thiansomboon, S. (2017). Factors causing default on housing loan: case study of commercial bank. Thammasat University.
- Thomas, S. S., George, J. P., Godwin, B. J., & Siby, A. (2023). Young adults’ default intention: influence of behavioral factors in determining housing and real estate loan repayment in India. International Journal of Housing Markets and Analysis, 16(2), 426-444.
- Van Gool, J., Verbeke, W., Sercu, P., & Baesens, B. (2012), Credit scoring for microfinance: is it worth it? International Journal of Finance & Economics, 17, 103-123.
- Zhu, L. (2019). Predictive Modelling for Loan Defaults. UCLA.
- Zhu, X., Chu, Q., Song, X., Hu, P., & Peng, L. (2023). Explainable prediction of loan default based on machine learning models. Data Science and Management, 6(3), 123-133.