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dc.contributor.authorLimsombunchai, Visiten
dc.contributor.authorGan, C.en
dc.contributor.authorLee, Minsooen
dc.identifier.citationLimsombunchai, 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. doi:10.3844/ajassp.2005.1198.1205en
dc.description.abstractLoan 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.en
dc.publisherScience Publicationsen
dc.relationThe original publication is available from - Science Publications -
dc.rights© 2005 Visit Limsombunchai, Christopher Gan and Minsoo Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en
dc.subjectagricultural loanen
dc.subjectcredit scoringen
dc.subjectneural networksen
dc.subjectlogistic regressionen
dc.titleAn analysis of credit scoring for agricultural loans in Thailanden
dc.typeJournal Article
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agribusiness and Commerceen
lu.contributor.unit/LU/Faculty of Agribusiness and Commerce/ECONen
lu.contributor.unitDepartment of Financial and Business Systemsen
dc.subject.anzsrc1502 Banking, Finance and Investmenten
dc.subject.anzsrc150205 Investment and Risk Managementen
dc.relation.isPartOfAmerican Journal of Applied Sciencesen
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce/ECON
pubs.organisational-group/LU/Faculty of Agribusiness and Commerce/FABS
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/PE20
pubs.organisational-group/LU/Research Management Office/QE18

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