Risk Assessment Strategies in Credit Process of Iranian Banking System Case study: Banks listed in Tehran Stock Exchange

Purpose and theoretical framework: This applied-descriptive research has been performed to the aim of identifying fraud risk assessment strategies in credit process of Iranian banking system. Statistical population of the research includes banks listed in Tehran Stock exchange. Design and methodology: To collect data, library and field studies have been used through distribution of questionnaire among participants i.e. university experts, as well as experts in the banking field and risk management. To analyze data, descriptive and inferential statistical tests such as factor analysis and structural equation modeling (SEM) have been used. Findings: In general, the research findings show that fraud risk assessment strategies in credit process have considerable effect on performance of operational risk management in credit process of Iranian banking system. Therefore, the results have been indicating that fraud risk assessment strategies in credit process of Iranian banking system have to be taken into consideration by economic activists, investors, government, and supervising institutions. Implications: Therefore, with consideration of the results from present research it is proposed that in credit field of banks, more attention would be paid to the issue of various types of fraud risks; and, future researchers are suggested to study the role played by internal control and corporate governance on operational risk management in credit process of banks listed in Tehran Stock Exchange, and to compare their results with those of present research for analysis. It goes without saying that, most important restrictions existing in this research and considered in interpretation of results include: low number of researches with similar subject, including those performed inside the country and out of it due to novelty of the subject.

Keywords:

stock trading strategy , minor factors , major factors , Markov-switching model