Credit Risk Portfolio Management System for Agricultural Lending of the Rural Financial Market in Thailand
Keywords:
Agricultural lending, credit scoring system, internal obligor rating systemAbstract
The main objective of this study is to develop the credit risk portfolio management system for agricultural lending of the Bank for Agriculture and Agricultural Cooperatives, an important organization of the Thai rural financial market. The LOGIT model and the Artificial Neural Network (ANN) models are first developed to identify the probability of default from the economical and geographical risk factors. The results verify the importance of the deficit irrigation, saving, land suitability, natural disasters (flood and drought), epidemic area and debt service ratio are important factors in determining of the probability of default in the debtors. The geographical risk factors incorporating into the models have the statistical significance and can be increased the efficiency to prediction power in discriminating the debtors. The models are tested for reliability and validity of the prediction power in discriminating the debtors. The study supports the use of LOGIT model to application of the credit risk management systems. It is found that the LOGIT model gives more accurately and lower misclassification costs than the ANN model. The results from the LOGIT model are subsequently employed to formulate the prediction equations of the probability of default, credit scoring systems and internal obligor rating systems with reference to the Basel II criteria. The results show how agricultural exposures can be managed on a portfolio basis which will enable the Bank to diversify the risk in each of portfolio share, determine the interest rate on the basis of risk, and analyze for the minimum capital requirements and optimal returns in agricultural loan portfolio.
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