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 Due to the necessity of identifying the possibility of fraud in financial reporting for the purpose of helping the market and information users to optimize the allocation of financial resources, and because of the key role of fraud in losing credit, increasing ambiguity, reducing stock liquidity, and reducing the fair allocation process in the market, which in itself widens the gap between shareholders, investors, the users of financial information, and managers, this study seeks to examine the effect of the probability of fraud on information asymmetry criteria in accepted banks in Tehran Stock Exchange. The statistical sample includes 15 accepted banks in Tehran Stock Exchange from 2012 to 2018 which would totally add up to 105 year-banks. This research adopts a multivariate linear regression model. The variables used as the information asymmetry criterion in this study include price gap and price impact. The findings uncover a meaningful and positive correlation between fraud, price gap, and price impact which by and of itself results in cultivating a positive and meaningful correlation between fraud and information asymmetry. In other words, as fraud probability increases, the information asymmetry increases too. The impact of fraud disclosure on information asymmetry undermines the company’s credibility and leads to growing uncertainty. Investors, in such conditions, doubt the dependability of investigation in the capital market, and constantly raise their required rate of return.