A survey on the application of the insolvency anticipation models (Altman, Fulmer, Springate, Zimijewski and Shirata) in anticipating the default of loans granted to the firms listed in Tehran Stock Exchange Case Study: Bank Sepah

Freezing the resources as nonperforming loans, by decreasing the power of banks in loan granting has a negating effect on productivity. One of the most important ways of preventing the creation of nonperforming loans is accurate decision making on the date of granting loans; so while identifying the customers exactly, the loans are granted optimally and then the probability of default may be decreased. For decision making, some various tools are used that the most important ones are the customer's validation and credit rating. In the developed banks, the customer's validation and credit rating are performed by modern data sets and soft wares, however, in the Iranian banks, validation is often performed based on professional judgment and expert diagnosis and just by using some financial ratios. Altman, Fulmer, Springate, Zimijewski and Shirata models are of the most popular models of insolvency anticipation. According to the findings of this survey, the Altman and Springate models are suitable to use in the customer's credit rating system. The results of studying the anticipating ability of these models indicate that there is a significant difference between the results of the mentioned models, and the Altman and Springate models possess the most powerful ability of anticipation.