Quarterly Studies in Banking Management and Islamic Banking

Abstract This study seeks to investigate the effect of variables and data related to people's social network on their credit score. In this study, two main goals are pursued: reducing information asymmetry and increasing financial inclusion. Achieving the above goals is done by finding meaningful information about people's social data to measure how such data affects their credit score. The basic hypothesis of this study is that people with a high credit score have social relationships with people who are similar and of the same age. A data set of more than 300,000 loans that have been paid by an Iranian bank to real people has been used to confirm and explain the effect of social network variables on credit score. In order to determine the variables of the study, an in-depth interview was conducted with a number of banking experts and people actively involved in the field of credit scoring, and at the end, the variables were determined and classified into three categories: financial, behavioral, and social. The study continued with the logistic regression method and finally with various regression models based on deep machine learning, including gradient. The results of the analyses conducted using the logistic regression method show that, statistically, people's social variables can predict the probability of their loan default. The results of machine learning algorithms also indicate that social network information can significantly improve the performance of loan default prediction.

Abstract Currently, one of the most important issues that banks and financial institutions always face is the issue of credit risk or the possibility of non-fulfillment of obligations by applicants receiving credit facilities. Therefore, the main purpose of this study is to explain and review the credit rating system of banks in facility files of companies receiving facilities based on the accepted regressions in credit risk and review of financial ratios of these companies. The statistical population of this research consists of 97 listed companies in the period from 2016 to 2021. The results show that the coefficients of independent variables in data mining techniques are higher and the logit model has a higher fit and is more accurate than the good probit model. Current ratios (CUR), turnover (STA) and return on sales (NTS) also play a greater role in assessing default risk.

Abstract Competition is one of the issues that economists always refer to as a solution for economic growth and optimal use of economic resources. Increasing competition and efficiency in the banking market can increase the quality and variety of banking services as well as reduce transaction costs. This is an important reason for the need to measure banking risks by considering the competition between them. In this study, for banks listed on the stock exchange, the relationship between banking competition and various types of important banking risks has been investigated. The risks in question are default risk, assets, market, capital and liquidity, which are measured as a dependent variable of their relationship with the competition index (Herfindal). The results show that banking competition has a negative and significant relationship with asset risk and a positive and significant