THE AIMS THIS PAPER IS ANALYZING THE EFFECT OF THE CREDIT RISK MODELS ENGINEERING ECONOMICS AND financial ENGINEERING. A MODEL HAS BEEN PRESENTED TO PREDICT CREDIT RISK AND CREDIT RATING CORPORATE CLIENTS THAT APPLYING FOR A COMMERCIAL BANK LOAN BY USING DATA ENVELOPMENT ANALYSIS AND NEURAL NETWORK AND LOGISTIC REGRESSION WAS PERFORMED TO COMPARE THE THREE MODELS. FOR THIS PURPOSE, SOME STUDIES HAVE BEEN DOUE ON financial AND NONfinancial INFORMATION USING A SIMPLE RANDOM SAMPLE OF N=146 CORPORATE CLIENTS FACILITIES APPLICANT WAS DONE. IN THIS STUDY, 27 VARIABLE THAT INCLUDES financial AND NON-financial DATA HAVE BEEN STUDIED. THEN THREE OF THEM USED FACTOR ANALYSIS AND EXPERT JUDGMENT (DELPHI METHOD) AND VARIABLE WERE ENTERED IN THE MODEL AND DEA EFFICIENCY SCORES LAW FIRMS. THE VARIABLE SELECTED AS THE INPUT VECTOR OF THE NEURAL NETWORK 3-LAYER PERCEPTRON MODEL AND FINALLY THE RELEVANT DATA WERE ANALYZED USING LOGISTIC REGRESSION TO ESTIMATE CREDIT RISK.