Examining determinants of loan default: An empirical analysis on credit factors in Thai savings and credit cooperatives

  • 56 Views
  • 22 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

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.

view full abstract hide full abstract
    • 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
    • Conceptualization
      Klangjai Sangwichitr, Panern Intara
    • Data curation
      Klangjai Sangwichitr
    • Formal Analysis
      Klangjai Sangwichitr, Panern Intara
    • Investigation
      Klangjai Sangwichitr, Panern Intara
    • Methodology
      Klangjai Sangwichitr, Panern Intara
    • Supervision
      Klangjai Sangwichitr, Panern Intara
    • Visualization
      Klangjai Sangwichitr
    • Writing – original draft
      Klangjai Sangwichitr, Panern Intara
    • Writing – review & editing
      Klangjai Sangwichitr, Panern Intara
    • Project administration
      Panern Intara
    • Validation
      Panern Intara