In the banking industry, one issue that must be always considered by the credit policy makers is risk management. Among various risks which banks are dealing with, credit risk is most important. It is caused by the losses of disability or lack of tendency of borrowers to pay their credit obligations. To manage and control the mentioned risk, credit rating systems are undeniable requirement. Such systems, according to existent documents and information, determine the credit score of customers and rate them based on amount of their risk on bank. It is evident that use of these systems helps bank to choose costumers in a good way. And through the control and reduction the credit risk, improves efficiency level of providing bank facilities. This study examines artificial intelligent based credit models consist of the artificial neural networks model, adaptive Neuro- fuzzy Inference Systems and a multi-objective fuzzy simplex-genetic algorithm which is developed to optimize the fuzzy rules in fuzzy inference system, are applied to predict bank legal customers financial performance. After collecting and examining data, 320 files related to legal customers of TEJARAT bank branches in Tehran over 2001-2006, debt ratio, operational ratio and return on equity ratio were selected as explanatory variables. And on the other side dependent variable was considered as a dummy variable, 0 for good credit and 1 for bad credit customers. Then data were divided in to model (in-sample) and test (out-of-sample) sets. After training and developing models, predictive performance of models is examined based on their sensitivity and specificity ratios on the test set. Empirical findings show that artificial neural network has highest accuracy at identifying defaults in the portfolio out-of-sample. Multi-objective fuzzy simplex- genetic algorithm, besides its good ability at identifying default/non-default cases, has two more advantages. First it is able to consider several objective functions in the training process and another is that its outcomes can be interpreted and find most effective explanatory variable on default. Analysis was shown that debt ratio is the most consistent predictor of default.