Iranian Journal of Economic Research

Abstract Traditional methods of deciding whether to grant credit to a particular individual use human judgment of the risk of default based on experience of previous decisions. However, economic pressures resulting from increased demand for credit, allied with greater commercial competition and the emergence of new computer technology have led to development of sophisticated statistical models to aid the credit granting decision making process. Credit scoring is the name used to describe this process of determining how likely applicants are to default with their repayments. Credit scoring has some obvious benefits that have led to its increasing use in loan evaluation. For example, it is quicker, cheaper and more objective than judgmental method. A wide range of statistical methods such as discriminant analysis, logistic regression, and neural networks have been applied for credit scoring. In this paper, we design a neural network credit scoring system for classifying the applicants of personal loans in bank and compare the performance of this model with discriminant analysis and logistic regression models. The results of this investigation show that the neural network model is more accurate and more flexible than discriminant analysis and logistic regression.

Abstract The aim of this study was to investigate factors affecting repayment of agricultural loan in FarsProvince, using the data collected from the Agricultural Bank and 163 questionnaires filled out in the Province of Fars. The farmers were divided into repayment and defaulter groups and discriminant analysis model was used.
    Discriminant analysis function results in Fars Province indicated that natural losses, proportion of farm income to total income, crop insurance, farm income, saving, expected time period, the Bank supervision, repayment period time, type of activity, diversity index, market situation, non-farm income, acreage, education and town dummy variables separate two groups of farmers.
    Among variables in discriminant analysis function, natural losses and expected time period for receiving credit caused the probability of loan repayment to decrease, but the other variables to increase. Finally, some recommendations are made to improve repayment tendency and ability of borrowers.