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The Front-End’s Lending Decision System for the Agricultural Bank in Thailand

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Abstract
The main objective of this study is to develop the front-end’s lending decision system of the Bank for Agriculture and Agricultural Cooperatives, a major lender in Thailand’s agricultural sector. The logit model and the artificial neural network model have been developed to reflect risk factors to identify the probability of default by each new borrower. The study supports the use of the logit model to develop the system because it gives more accuracy in predicting the probability of default and debtor classification than the artificial neural network model. The working process of the system is classified into two sections including credit risk management, which is the process of screening the loan applications and setting the credit approval or rejection criteria, and affordability risk management, which is the process of determining the maximum loan amount for the debtor who has passed the credit approval criteria. In this study, the author caps the debt service ratio as a threshold for determining the amount of credit (the loan amount approved and interest expense) at 70% and determines that the maximum loan principal is 63% of the debtor's total annual income. The system is also used as an instrument to support the implementation of appropriate credit policies in handling agricultural households’ excess debt and promote the building of financial discipline for agricultural households in the rural sector of Thailand.

Suggested Citation

  • Unknown, 2021. "The Front-End’s Lending Decision System for the Agricultural Bank in Thailand," Asian Journal of Applied Economics, Kasetsart University, Center for Applied Economics Research, vol. 28(2).
    • Handle: RePEc:ags:thkase:334389
    DOI: 10.22004/ag.econ.334389
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    References listed on IDEAS

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    1. Songkran Somboon, 2015. "Credit Scoring System for Managing Risk in Agricultural Loan Portfolio of the Thai Rural Financial Market," Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, vol. 22(1), pages 27-50, June.
    2. Barney, Douglas K. & Finley Graves, O. & Johnson, John D., 1999. "The farmers home administration and farm debt failure prediction," Journal of Accounting and Public Policy, Elsevier, vol. 18(2), pages 99-139.
    3. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
    4. Wu, Chunchi & Wang, Xu-Ming, 2000. "A Neural Network Approach for Analyzing Small Business Lending Decisions," Review of Quantitative Finance and Accounting, Springer, vol. 15(3), pages 259-276, November.
    5. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    6. Calum G. Turvey & Alfons Weersink, 1997. "Credit Risk and the Demand for Agricultural Loans," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 45(3), pages 201-217, November.
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    Keywords

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