Quarterly Studies in Banking Management and Islamic Banking

Abstract One of the major problems of the banking system in recent years is the creation of non-current claims. In fact, a bank's operating profit, which includes the profit received for granting facilities to customers, cannot be realized due to the creation of bank claims, and the circulation of money and facilities becomes stagnant. Therefore, in order to adjust bank claims in the future and, considering interest rates, to estimate the profit function of bank customers using the customer lifetime value improved with Markov chain, the researchers present a model. Since there are eight different types of facilities for customers in the researched branches of Mellat Bank, information was obtained separately for each of these facilities, including the profit and customers’ claims, and for each loan a function was obtained. As such, each of these facilities had a specific function and a specific profit and loss for the bank. After obtaining the profit and loss of each one, for validation purposes, the researchers compared them with the previously obtained, as well as settled, facilities and analyzed the results.

Abstract Banks play an important role in the country's economy, which includes equipping resources, intermediation and facilitating the flow of payments and allocating credit to the recipient of facilities. In this way, the bank faces many risks that can be classified into four general categories: credit, liquidity, operations and investment.
The most important risk that banks face is credit risk. Credit risk arises due to the possibility of non-timely repayment of facilities and interest accrued to it.
Banks have always faced the challenge of how and based on what criteria and methods to evaluate credit applicants (facilities and liabilities)? This can be done through a comprehensive, structured system and selection of techniques. This research also seeks an expert proposal and solution to solve this problem in order to be able to categorize the applicants of financial credits well and reduce the possibility of non-repayment of the granted facilities. Obviously, the implementation of these methods and techniques can lead to the design of rapid alert systems for banks to be aware of the status of their credit portfolio and facilities and have knowledge of their customers.
In this article, using logit and probit models (regression models), the factors affecting the risk of customer default are investigated. Research findings indicate that the Probit model is more accurate in predicting customer default risk than the Logit mode.

Abstract The role of banks in the economy of any country is determined by the economic structure of that system. Since the type of economy in Iran is a money-oriented and so-called bank-oriented market, the importance of banks in the economy of Iran is very different. To achieve the corporate mission on the one hand and social responsibility, on the other hand, banks must fulfill this role by changing the composition of their assets. Today, one of the main problems of banks is the amount of their non-current receivables. The ratio of non-current receivables to lending facilities in the banking system is a criterion that is used to measure the health of the bank. Non-current receivables of banks can be the source of many financial and monetary crises in the world and can have many adverse effects on banks, various economic sectors, and on a larger scale, on the people of any country. Undoubtedly, in recent years, the issue of non-current receivables has become an acute issue for the banking system, and the emergence of this crisis in the financial system can lead to the outflow of depositors' savings from banks and, consequently, their bankruptcy. Therefore, those involved in the banking system are seeking to solve this problem. In this regard, the first step to solving such a problem is to identify the factors affecting the increase of non-current receivables and rank these factors. Therefore, the purpose of this study is to identify and rank the factors affecting the increase in non-current receivables of public, private and Banks subject to principle 44 in Iran empirically.

Abstract The purpose of this study is to validate customers and the impact of its components on non-current receivables of Bank Mellat. For this purpose, in the first stage, first, by using the documentary and library method, 15 indicators affecting the validation of customers in three sections: individual, financial and social identified, and then the final data related to the indicators, including the files of 7330 real customers of Bank Mellat, were collected during 2013-2019. To study the credit rating indicators of customers and the impact of its components on non-current receivables of Bank Mellat, multiple logistic regression was used in four categories of timely receipt, past due, delinquent and doubtful receivables. The results showed that in general, individual, social, and economic conditions affecting the credit of real customers have a significant effect on reducing the non-current receivables of Bank Mellat. Using the results of research model estimation can help bank managers and monetary and banking policymakers to make the right decision regarding customers' credit ratings and reduce banks' non-current receivables.

Abstract      Non-performing loans are one of the most important data used to determine the efficiency of banks as well as the health of banking sector and the effects of financial crisis. In recent years, the increase in non-performing loans needs for more rigorous researches. On the other hand, economic activities have been facing considerable fluctuations in major macroeconomic indicators.     This study, therefore, aims at investigating the impact of fluctuations in macroeconomic variables on non-performing loans over the years 1978-2015 (during the Islamic revolution period) in Iranian banks.     In order to capture the aforementioned relationship, an EGARCH model has been employed. To meet the econometric validity requirements, vector auto regression (VAR) has been used to fit the proper model.     It is shown that the macroeconomic indicators have significant impacts on NPLs, more precisely the ratio of non-performing to total loans. According of the results, most of the long term changes in NPLs are explained by their past values and other important variables including oil revenue, budget deficit, inflation, GDP and unemployment rate.