نشریه مطالعاتی در مدیریت بانکی و بانکداری اسلامی
پیاپی 22 (بهار 1402)

In recent years, credit scoring has been one of the main methods of financial institutions to assess credit risk. The main problem that limits the effectiveness of credit scoring methods is the unbalanced distribution of data, which means that in the data set, the number of samples of good customers is far more than the number of samples of bad customers. This study deals with the credit scoring of customers active in the tourism industry using an entropy-based algorithm with the aim of overcoming the problem of data imbalance, which evaluates the data in terms of the entropy of customer validation indicators and defines a criterion that can measure how good or bad a customer is by considering only the good cases of the data set and the sample of the applicants for facilities. In this research study, 204 active customers of the tourism industry of Bank Melli Iran were selected as the data set. The results showed that the entropy model has a good prediction power and is an effective model for validating customers.

One of the most important concerns of lenders for granting loans and credits is the borrower's ability to repay the principal and interest of the loan, as well as credits. As such, in order to provide financial resources through debts to banks, according to the capital structure of companies and enterprises, the horizon of institutional investors has been examined. One of the solutions is to review the financial statements in order to check the company's ability to repay the facility. But the inappropriate quality of financial statements can increase the risk of predicting the loss of receivables. To measure the horizon of institutional investors, the turnover rate of buyers and sellers has been used, and to measure bank debts, the ratio of bank debt to total debt has been used. By selecting a sample of 135 companies admitted to the Tehran Stock Exchange, during the period from 2012 to 2018 and using the regression model, the research results indicated that there is a negative relationship between the horizon of long-term institutional investors and financing through banking sources. With the presence of institutional shareholders with long-term horizons, the amount of financing through bank debt decreases. Also, there is a positive relationship between the horizon of short-term institutional investors and bank debt financing. In other words, with the presence of institutional shareholders with a short-term horizon, the amount of financing through bank debt increases.

The current research has been carried out with the aim of finding complications in the policy of resource allocation in governmental banks. This research is practical based on its aim and was implemented in three steps through a multi-stage process. The first step was to identify and explain the research plan. In the second step, according to the findings of the first step, the interviews were compiled first, and then, using the qualitative approach (Delphi method), the modeling of various aspects of problem solving and the implementation of policies in the field of monetary resources and its allocation were discussed. Academic experts and experts in the subject of the research, as well as experts from Bank Melli Iran and Sepah Bank, made up the sample. Using purposive sampling, 15 people were selected in this department. The main tool of data collection was in-depth interviews. To increase the internal reliability, triangular methods, member checks, and pairwise checks were used, which showed the calculated Kappa coefficient of 87% agreement. Based on the findings, damages and complications under the headings of policy implementers, structure of laws and policy implementers, resources and facilities, organizational policies, legal policies, systemic corruption, pressure groups, related stakeholders, economic and internal processes indices, the damage of the policy making process were identified. The results revealed that the structure of the banking system needs fundamental changes. Therefore, the new structure should be adapted to the goals defined in the laws for the organization, and human resources should be recruited based on that, so that the laws and policies can be implemented well in the new context.

Queues are one of the pillars of queuing systems that customers often encounter. Systems modeling and the use of computer simulation in queuing systems reveal the hidden realities of systems. Using and applying different methods makes it possible to reduce the length of the queue and the waiting time of customers, and in general, this improves the state of the system. A bank branch is one of the places where most customers experience waiting in queues. In branches that have many customers, account opening and debit card issuance always cause a lot of accumulation due to the many steps and applicants, which results in dissatisfaction. As such, most branches take different measures to face this issue so they have the best work efficiency and also reduce queue pressure. In this article, the entry and exit information of 12,300 customers from four Mellat Bank branches was collected for 30 days, and statistical analysis was performed using Minitab software. To this aim, the queue behavior was analyzed, modeling and programming were performed, and computer simulation with R software and using the power of time extension for 100 working days was conducted. As a result, the strengths and weaknesses of different service models were identified in the banking queue system so that the best decision can be made for the service delivery pattern.

Financial institutions, including banks, are considered as the vital element of an economic system. Therefore, banks play a vital role in the economy of a country. On the other hand, economic sanctions cause problems in banking and other economic sectors of the country. Financial health and profitability of banks are among the factors that have been affected by economic sanctions. Economic sanctions can affect the financial health and profitability of banks in different ways. Therefore, in this research, the effect of economic sanctions on the relationship between the banking health index and the profitability of banks during the time period of 1390-1400 has been investigated with the panel data regression method. In addition, in this research, the effect of some macroeconomic changes such as economic growth rate, inflation rate, and liquidity on banks' profitability have been investigated. The findings revealed that sanctions had a negative and significant effect on the relationship between the banking health index and banks' profitability. Another important finding is that the macroeconomic variables used in the research, except for the liquidity variable, which had no significant effect on banks' profitability, had a positive and significant effect on banks' profitability.

The purpose of the present research is to design a credit portfolio optimization model in the banking industry using a meta-heuristic algorithm. Risk is one of the basic concepts in financial markets, which has a certain complexity. Due to the lack of an accurate picture of risk realization, financial markets need risk control and management approaches. The current study is descriptive in terms of data collection and developmental-applicative in terms of purpose. The statistical population of this research includes all the facility files over the last 10 years, as well as the financial statements of the branches of one of the commercial banks in Iran, which were selected through the census method. The risk criteria used in the models are the values at risk of the fuzzy average. Research models were implemented using Pareto Strength Evolutionary Algorithms (SPEA-II), Non-Globular Ranking Based Genetics (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The software used in the research is MATLAB. The results show that the NSGA-II algorithm has a better performance compared to the other two algorithms in terms of the quality metric, the diversity metric, and the spacing metric, both in small and large sizes. Also, the SPEA-II algorithm performs better than the other two algorithms in terms of the Mean Ideal Distance in both small and large scales, and the MOPSO algorithm in terms of time.