Quarterly Studies in Banking Management and Islamic Banking

Abstract This article aims to design a conceptual and indigenous framework for the restructuring of Islamic banking in deprived areas of Iran, focusing on achieving financial justice, promoting banking inclusion, and social empowerment. Regions such as Sistan and Baluchestan, Kohgiluyeh and Boyer Ahmad, Ilam, and parts of Khuzestan are clear examples of banking deprivation in Iran due to a combination of structural poverty, institutional distrust, and lack of financial infrastructure. The present study attempts to design a five-dimensional framework to present an Islamic, indigenous, and sustainable solution to address banking challenges in these regions. Despite the jurisprudential and justice-oriented foundations of the Islamic banking system, its performance in these areas has been ineffective due to the lack of adaptation of its structures and processes to the cultural and institutional characteristics of deprived areas. This study, relying on the principles of Islamic economic justice and using the guided content analysis method, presents a conceptual and indigenous framework for the restructuring of Islamic banking operations. Data were collected through 18 semi-structured interviews with experts in the fields of economic jurisprudence, Islamic banking, and regional development. The final conceptual framework is based on five complementary dimensions: local institution building, simplification of contracts and processes, training local human resources, social validation, and targeted policy support. These dimensions, in interaction with each other, pave the way for promoting financial inclusion, increasing social trust, and economic participation. The innovation of this framework lies in creating a systematic link between the principles of Islamic justice and the operational dimensions of banking. Despite its focus on localization, this framework is also applicable to similar Islamic societies. Inspired by the experience of Grameen Bank in Bangladesh in collective guarantee lending and Islamic microfinance programs facilitated by Islamic Relief Indonesia in local institutions, this framework provides a new framework for achieving financial justice in deprived areas.                                                                                                                                                                                 

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.