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?^(-4) 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.

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?^(-4) 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.

This paper presents a comparative study of using a linear probability and Logit models to predict credit risk of the customers in some branches of Bank Mellat in Tehran, Iran. The statistical population of this research includes the applicants of the facilities granted by Bank Mellat in Tehran during the year 2008. Each branches of Bank Mellat of Tehran has been considered as a cluster, where a sample has been taken using simple random method. The sample size consists of 176 companies, 109 legal entities are classified as those ones good at settling their accounts, and 67 as those ones tardy in settling their accounts. The financial ratios of these companies have been calculated based on their audited financial statements and by descriptive and analytical methods of two statistical models. The results show that liquidity ratios are not significant factors for the prediction of credit risks and these two models are not significantly different from each other in this term. Moreover, the accuracy values of credit risk prediction of linear and Logit models are 73.7 percent and 80.3 percent, respectively. Therefore, Logit model is more consistent with reality and more appropriate for such a prediction.

An integrated risk management is a process, which enables banks to measure and manage all risks, simultaneously. The recent turbulent chaos on banking industry has increase the relative importance of risk management, more than before. This paper investigates the relationship between credit risk and liquidity risk among Iranian banks. The proposed study includes all private and governmental banks as population over the period 2005-2012. The results Pearson correlation has disclosed a positive and meaningful relationship between credit and liquidity risks. Bank size also impacts on two mentioned risk factors but we there seems to be no relationship between financial chaos and type of ownership with risk factors.

Banks are principally in the interest earning business. The interest earning nature of banks comes with the amount of loans that banks are able to advance to their customers. To ensure that the stream of interest is not treacherous, banks must put in place stringent credit risk management practices. In this study, we investigate credit risk and default among Ghanaian banks and how these banks are coping with such pressures. Using a survey method, we found that though varied in nature, all the banks have some form of credit management procedures put in place to manage their loan portfolios. We found loan application processes to be bank specific. However, there are some common requirements that banks usually demand from customers in the process of assessing their suitability for a loan. We also found most of the credit management practices of banks to be consistent with the CAMPARI model. We recommend that the Central Bank facilitate in the establishment of a vibrant credit-referencing bureau in order to provide credit history of customers of the banks.

Consumer credit risk assessment involves the implementation of risk assessment techniques to manage a borrower’s account from the event of pre-screening a potential application through to the management of the account during its life-cycle and possible write-off. This paper presents an empirical investigation to study the relationship between 13 different factors such as credit history, applications’ educational and management skills, etc. and credit risk. The study selects the profiles of 380 applicants who received loans from one of Iranian banks named Bank Mellat in city of Tehran, Iran over the period 2010-2011. Using Pearson correlation, the study has determined a meaningful relationship between applicants’ profiles including credit history, business characteristics, personal characteristics, etc. and credit risk.

These days, there are increasing changes on environmental and economic networks and different risks of various institutions affect the financial structure. Different institutions including financial and credit institutions are facing with the risk of lack of their timely obligations to make sure the repayment of the funds is granted. In this study, the effects of economic factors not affected by intentional behavior of customers are investigated. Statistical study of the banking system includes all public and private banks. Statistical research community from 2005 to 2010 is considered. The cross-sectional data of the study and a combination of regression analysis is used. The regression analysis of combined data, fixed effects model based on the data is a cross-sectional fit. According to results of regression analysis, Pearson and Spearman & apos; s Correlation Coefficient, there is no significant relationship between the inflation rate, employment rate, unemployment rate, the dollar, the euro, with import growth of credit risk in the banking system in Iran. Therefore, based on probability theory, it can be stated that the credit risk in the banking system in Iran under the influence of variables is not mentioned. In addition, positive and significant relationship between stock index and credit risk in the banking system in Iran has increased by Weber in this index increases and reducing credit risk is reduced.

One of the most important factors of survival of financial institutes and banks in the current competitive markets is to create balance and equality among resources and consumptions as well as to keep the health of money circulation in these institutes. According to the experiences obtained from recent financial crises in the world. The lack of appropriate management of the demands of banks and financial institutions can be considered as one of the main factors of occurrence of this crisis. The objective of the present study is to identify and classify customers according to credit risk and decisions of predictive models. The present research is a survey research employing field study in terms of the data collection method. The method of collecting theoretical framework was library research and the data were collected by two ways of data of a questionnaire and real customers’ financial data. To analyze the data of the questionnaire, analytical hierarchy process and to analyze real customers’ financial data, the TOPSIS method were employed. The population of the study included files of real customers in one of the branches of RefahKargaran Bank in city of Tabriz, Iran. From among 800 files, 140 files were completed and using Morgan’s table, 103 files were investigated. The final model was presented and with 95% of probability, if the next customer’s data is entered the model, it will capable of identifying accurately the degree of customer risk.