Research Article Open Access

An Analysis of Credit Scoring for Agricultural Loans in Thailand

Visit Limsombunchai, Christopher Gan and Minsoo Lee

Abstract

Loan contract performance determines the profitability and stability of the financial institutions and screening the loan applications is a key process in minimizing credit risk. Before making any credit decisions, credit analysis (the assessment of the financial history and financial backgrounds of the borrowers) should be completed as part of the screening process. A good credit risk assessment assists financial institutions on loan pricing, determining amount of credit, credit risk management, reduction of default risk and increase in debt repayment. The purpose of this study is to estimate a credit scoring model for the agricultural loans in Thailand. The logistic regression and Artificial Neural Networks (ANN) are used to construct the credit scoring models and to predict the borrower’s creditworthiness and default risk. The results of the logistic regression confirm the importance of total asset value, capital turnover ratio (efficiency) and the duration of a bank - borrower relationship as important factors in determining the creditworthiness of the borrowers. The results also show that a higher value of assets implies a higher credit worthiness and a higher probability of a good loan. However, the negative signs found on both capital turnover ratio and the duration of bank-borrower relationship, which contradict with the hypothesized signs, suggest that the borrower who has a long relationship with the bank and who has a higher gross income to total assets has a higher probability to default on debt repayment.

American Journal of Applied Sciences
Volume 2 No. 8, 2005, 1198-1205

DOI: https://doi.org/10.3844/ajassp.2005.1198.1205

Submitted On: 18 July 2005 Published On: 31 August 2005

How to Cite: Limsombunchai, V., Gan, C. & Lee, M. (2005). An Analysis of Credit Scoring for Agricultural Loans in Thailand. American Journal of Applied Sciences, 2(8), 1198-1205. https://doi.org/10.3844/ajassp.2005.1198.1205

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Keywords

  • Agricultural loan
  • credit scoring
  • neural networks
  • logistic regression