ریسک اعتباری

One of the most important issues in the banking industry is risk issues and risk management. At the top of the nancial system of any economy is the banking industry, which in developing countries has the largest role in nancial intermediation, therefore one of the biggest tasks of the bank is to create a balance in correct assessment and risk management so as to create sustainable prots and value for shareholders. The purpose of this study was to investigate the eect of credit risk, liquidity risk, and capital adequacy ratio on the protability and resilience of Iranian banks using the panel data method during the period 2016 to 2021. The statistical population of this study includes 27 banks in the country that are operating with the permission of the Central Bank. In the present study, the variables of credit risk, liquidity risk, and capital adequacy ratio are considered as independent variables while protability and resilience are considered dependent variables, and data related to these variables are extracted from the website of the Central Bank and Iran Banking Higher Education Institute. At rst, the signicance of the data was investigated and the result showed that the data are signi cant. Also, based on the Chow and Hausman tests, the appropriate model for the problem was selected. A data normality test was also performed and the results show that all data are normal. Then, by performing the tests of error independence, heterogeneity of variance, and alignment of independent variables, the nal model of the research was tted. The results showed that credit risk did not have a signicant eect on protability but had a negative and signicant eect on resilience and also liquidity risk had a signicant negative eect on both protability and resilience. Among these, the capital adequacy ratio has a positive and signicant eect on protability and a negative and signicant eect on liquidity.

The credit risk and non-performing loans of banks are among the most important problems of the banking system in Iran. And according to available statistics, the average default of loans and non-performing loans of banks in Iran is much higher than the global average. The main purpose of this study is to identify and analyze the basic and important factors affecting credit risk in the Iranian banking system.

For this purpose, first, using the method of guided interviews with twenty experts and credit risk managers of the country's banking sector, who were selected by the snowball method, the most important factors affecting credit risk were identified; then, these factors were returned to the experts for ranking in the form of a pairwise comparison questionnaire, and finally, the important factors affecting this risk, along with their sub-factors were analyzed using denp technique (the dematel based analytic network process).

The results of the research show that the macroeconomic factors are the most important factor as well as instability in macroeconomic environment, failure to take timely and inappropriate actions with defaulters, poor and inadequate credit monitoring, ordered loans, and lengthy and time-consuming judicial procedures are the most important factors affecting risk credit in Iranian banking.

Originality/Value: 

The distinguishing feature of this study from other similar studies is the use of DANP technique to investigate the relationships between factors and sub-factors.

Implementing a credit rating system considering banks' deferred claims is one of the most important means of controlling credit risk in banks and financial institutions. In the case of banking facilities, the possibility of non-repayment of facilities is one of the most important issues. By identifying various factors that affect the non-repayment of bank facilities, it is possible to provide a framework for reducing and controlling the credit risk of banks and improving the crediting process. The purpose of this paper is to examine the relationship between efficiency and risk in the banking system.

In this research, a sample of 24 companies from the most important legal customers of 11 branches of the Melli Bank of the city of Arak has been studied. 18 variables affecting credit risk were identified in this paper. Among the variables available, 6 variables were selected using the Factor Analysis Technique and Expert judgment (Delphi method), of which 3 were inputs and 3 were formed the outputs

The efficiency and rank of legal firms were obtained with the help of Data Envelopment Analysis models, and then using the Fitch Institute's data and the efficiency of legal clients, the credit rating of each of the legal firms and their qualitative analysis was expressed.

 Accordingly, using the method of data envelopment analysis and data provided by the Fitch Ratings Institute, the credit risk of Arak's Melli Bank's legal customers is assessed and ranked

In simple terms, banks operate in two areas of equipment and allocation of resources. Meanwhile, by taking into account the credit risk of customers, banks provide customer demands based on their requested facilities. One of the most important problem of management of loan portfolio is bankruptcy and bank failure. So, one of the most important techniques of financial and banking sections, conspicuously noticed, is the technique of risk management.
With the aim of selecting the optimal model and effective variables to rank customers credit, this study is presented by the models used in this research including Neural Networks back propagation the error, neural network algorithms, neural networking ``GMDH'', neural network algorithm with radius axis, ``Logit'' model, ``Probit'' model, and discriminate analysis model. This paper analyzes the internal and external factors influencing credit risk of ``Ayandeh bank''. For this purpose, 200 cases of actual customers of the state-owned banks were selected during seasonal intervals of 2006-2011 (1385-1390) to provide an effective strategy for reducing the risk and help to improve the implementation of the decision-making in ``Ayandeh bank''. In this data, we have 200 customers whom 105 of them were ``creditworthy customers '' and 95 of them were ``uncreditworthy customers ''. In the first phase, 9 variables were recognized as ineffective and five of them were removed. Finally, the comparison of these models show that neural network algorithms and neural network-centric radius ``GMDH'' have the highest accuracy in predicting the credit behavior of banking customers.

Banks as financial institutions must estimate the credit risk of their debtors. This is the basis of pricing a loan, determining appropriate interest rates and determining the mortgage required to each borrower. Since the continuity of bank activities largely depends on the amount of credit losses in a particular period, banks should consider the credit quality of their loan portfolio as a collection of debts.In this paper, the calculation of credit risk of corporates applying for loans has been investigated. Using fuzzy analytic hierarchy process, effective criteria for credit risk have been analyzed.The neural network is used to extract an open box model that describes the relationship between effective criteria and the credit risk of the companies who apply for a loan. Neural network model has been run with historical data. Observations have been based on 174 corporate who had taken out a loan from a major Iranian bank named Mellat (All loans had been made during 2005 to 2008).The output of the model can predict credit risk of a corporate by at least 84% accuracy.