One of the most important issues always facing banks and financial institutes is the issue of credit risk or the possibility of failure in the fulfillment of obligations by applicants who are receiving credit facilities. The considerable number of banks’ delayed loan payments all around the world shows the importance of this issue and the necessary consideration of this topic. Accordingly, many efforts have been made for providing an efficient model for more accurate evaluation and classification of applicants receiving credit facilities for valid decision making about granting or not granting these facilities to them. Different statistic methods have been applied for this purpose, such as Discriminant Analysis, Probit Regression, Logistic Regression, Neural Network and so on. Among these methods, Neural Network has been considered mostly because of its high flexibility in recent years. In this research, many efforts have been made to examine the efficiency of Logistic Regression and Neural Network models for credit decision of natural applicants receiving installment loans for selling in Tose-Taavon Bank, Guilan. For this reason, customers who had applied for loans from the beginning of 1388 (2009) to the end of 1392 (2013) and also had complete information files were 376 cases and reviewed based on the independent variables of this research such as applicant’s income, facility profit, repayment period, the amount of guarantor’s loan, and the type of assurance taken. The result of this survey shows that Logistic Regression and Neural Network models are both highly efficient for predicting applicants’ credit risk, but comparing these two models shows that  Neural Network is more efficient and more accurate.