mohammadreza poorfakharan

Blockchain technology, with its advantages such as reducing intermediaries, controlling costs, and increasing security, has garnered attention from the financial industry. This technology, besides centralizing banking processes, can help improve operational efficiency. This study aims to identify and analyze the drivers influencing the future of banking in Iran, with a focus on blockchain technology. The research is applied in terms of orientation and quantitative in terms of methodology. Two methods, fuzzy Delphi and Marcus, were used for data analysis. Initially, 29 drivers were extracted through a literature review and interviews with banking experts. Subsequently, nine drivers were selected for final prioritization using the Marcus method after distributing expert questionnaires and screening them with the fuzzy Delphi method. The final drivers were ranked through a questionnaire distribution. The perceived organizational benefits, the compatibility of blockchain technology with the IT systems of the banking sector, the development of RegTech in the country, and the regulatory approaches of lawmakers regarding digital financial technologies were, respectively, identified as the highest-priority drivers. The most important practical recommendations of the study include developing a tool to measure the benefits of blockchain technology, adopting a systemic view towards the implementation of new technologies, making necessary changes before transferring the technology to the banking sector, leveraging RegTech capacities to assess the risks of using digital financial technologies, and ensuring the integration of regulatory policies and comprehensive regulation.

This study aims to investigate and predict information quality ranking using factor analysis and artificial intelligence in firms listed on the Tehran Stock Exchange for nine years from 2011 to 2019. The independent variable used in this study is the financial criteria of the firm, and the dependent variable is the quality measurement criteria of accounting information, in which all criteria have been converted into a single variable according to the factor analysis method. The present study is considered empirical accounting research, and the artificial intelligence method has been used to test the research hypotheses. The results indicate that according to the variable selection method of artificial intelligence, neighbourhood analysis among performance variables, including "Accounts receivable to sales ratio criteria", "Firm size", "Financial risk", "Current assets to total assets ratio", and "Cost to Sales ratio ", firms have the highest correlation with information quality rating. Other results indicate that linear and nonlinear artificial intelligence methods can predict accounting information firms' quality ratings on the Tehran Stock Exchange. Due to the importance of financial information quality in financial reporting, and innovation of the present study is the simultaneous use of all information quality criteria and artificial intelligence to examine research hypotheses

This study examines the ranking of information quality ranking with the approach of factor analysis and artificial intelligence in companies listed on the Tehran Stock Exchange. The independent variable used in this research is the criteria of the management system and the dependent variable of this research is the criteria of accounting information quality, which based on factor analysis method, all criteria have been converted into a single variable. The present study is part of experimental accounting research and artificial intelligence method has been used to test the research hypotheses. The results indicate that according to the variable selection method of neighborhood analysis, among the variables of the management system, the percentage of institutional owners "," dual role of CEO "," CEO tenure "," management ownership "," government ownership "have the highest correlation with rank Other results of Jackie's research are that the linear and non-linear artificial intelligence method pls has a high ability to predict the quality rating of accounting information of companies listed on the Tehran Stock Exchange.