*Corresponding author. E-mail addresses: gt_a23@yahoo.com (G. Taghizad) © 2014 Growing Science Ltd. All rights reserved. doi: 10.5267/j.msl.2014.7.007 Management Science Letters 4 (2014) 1765–1772 Contents lists available at GrowingScience Management Science Letters homepage: www.GrowingScience.com/msl Credit risk assessment: Evidence from banking industry Hassan Ghodrati and Gholamhassan Taghizad * Department of Management and Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran C H R O N I C L E A B S T R A C T Article history: Received January 20, 2014 Accepted 5 July 2014 Available online July 7 2014 Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86 × 10 ସ and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk. © 2014 Growing Science Ltd. All rights reserved. Keywords: Credit Risk Artificial Neural Network Default Risk Macroeconomic Variables Iranian banks 1. Introduction The globalization of financial markets along with remarkable growth on knowledge has created complexity in banking activities. In the past, banks’ main operations are normally limited in receiving deposits and granting facilities for gaining interests (Kiss, 2003; Gan & Lee, 2005). Banks’ competition development is on decreasing the borders of their traditional activities interests. The emergence of new financial markets and omission of traditional borders between banks and non- banking institutions along with financial crisis happened occasionally in international level, have drawn financial authorities and bankers’ attention to themselves. Banks are in the exposure of various types of risks based on their own activities. The risk of banking activities is generally divided into the two parts of intra organizational and extra organizational ones. In banking industry, economic activity risk includes credit, liquidity, commercial, financial risk, income and prices structural risk, and the risks caused by banking debts and assets structure. These kinds of risks can be omitted via right