Fraud risk assessment is an integral part of audit process, which is reviewed continuously until the end of the audit process because auditors are responsible for the type, manner, and extent of procedures used in the audit process to hedge fraud risk. Therefore, their own characteristics can influence the process of fraud risk assessment. The purpose of this study is to evaluate the characteristics of auditors to reduce the risk of fraud in corporate financial reporting. The sample consists of 87 firms listed in Tehran Stock Exchange during the years 2011-2018, being surveyed in a descriptive-correlational manner using Logit regression. The results show that, in firms with a longer audit tenure, as well as in firms with auditors specialized in industry, the risk of fraud is more likely, however, in firms audited by large companies, risk of fraud is less likely.

The purpose of this study was to investigate the relationship between weaknesses in internal controls and the risk of fraud in financial reporting. There are two perspectives on the effectiveness of internal controls as an important part of the governance system for non-fraudulent conduct. According to the first view, the strengthening of internal controls reduces the occurrence of fraud, and according to the second view, because of the ability of managers to override internal controls, internal controls do not have the necessary effectiveness in terms of fraud. In order to achieve the research purpose, using sample data from 152 companies listed in Tehran Stock Exchange during the period 2012 to 2017 and Logit regression approach to test the research hypotheses were studied. Benish's (1999) model was used to measure the risk of fraud in financial reporting. The findings suggest that, despite weaknesses in internal control and weaknesses in internal control, the risk of fraud in financial reporting increases. These findings are in line with the first view.

The purpose of this paper is to investigate the effect of religiosity on financial reporting fraud by emphasizing the mediating role of accountants' professional ethics in Iran. The research variables were measured through a questionnaire. For this purpose, 400 questionnaires were distributed among the members of the official experts of accounting and auditing justice throughout Iran. Finally, 312 items were accepted for analysis. Structural equation modeling (SEM) was used to test the hypotheses and Amos software was used for analysis. Findings showed that the religiosity of accountants has a negative and significant effect on financial reporting fraud and has a positive and significant effect on the professional ethics of accountants. Also, religiosity causes accountants to have a higher level of ethics, and this higher level of ethics is an effective factor in reducing financial reporting fraud by accountants, which shows the professional ethics of accountants, explains the relationship between their religiosity and reporting fraud.

Purpose: The purpose of this research is to examine the relationship between fraud in the company's financial reporting and fraud in the financial reporting of peer firms active in a similar geographic region, taking into account the moderating effect of competition in the industry.Research Method: This research is an applied study with an inductive approach and based on panel data analysis. In this research, the information of 11 listed petrochemical firms active in Pars Energy Special Economic Zone during 2011 to 2022 was used for 12 years. Also, for further analysis, the information of 32 listed firms active in petrochemical industry during the same time period has been used.Results: The results showed that fraud in the financial reporting of active firms in the same geographic region has a positive and significant relationship with fraud in the company's financial reporting. Competition in the industry does not have a significant effect on the intensity of the relationship between fraud in the financial reporting of firms operating in the same geographic area with fraud in the company's financial reporting, but competition in the industry increases the effect of the relationship between fraud in the financial reporting of firms operating in the same geographic region on fraud in reporting. The company is financed.Conclusion: The findings of this research showed that ethical misconduct and fraudulent financial reporting do not occur in a vacuum, and that neighboring firms have an impact on the willingness to commit violations and fraud in financial reporting.Contribution This study provides valuable insights into the factors influencing financial reporting fraud, focusing on peer companies in Iran, to provide stakeholders.

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2011 and 2016. The results show, that artificial neural network perform well relative to a Bayesian network, Discriminant Analysis, logistic regression and Support vector machine. The results also reveal some diversity in predictors used across the classification algorithms. Out of 19 predictors examined, only nine are consistently selected and used by different classification algorithms: Employee Productivity, Accounts Receivable to Sales, Debt-toEquity, Inventory to Sales, Sales to Total Assets, Return On Equity, Return on Sales, Liabilities to Interest Expenses, and Assets to Liabilities. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.

The main objective of this study was to present a model to detect financial reporting fraud by companies listed on Tehran Stock Exchange (TSE) using genetic algorithm. For this purpose, consistent with theoretical foundations, 21 variables were selected to predict fraud in financial reporting that finally, using statistical tests, 9 variables including SALE/EMP, RECT/SALE, LT/CEQ, INVT/SALE, SALE/TA, NI/CEQ, NI/SALE, LT/XINT, and AT/LT were selected as the potential financial reporting fraud indexes. Then, using genetic algorithm, the final model of fraud detection in financial reporting was presented. The statistical population of this study included 66 companies including 33 fraudulent and 33 nonfraudulent companies from 2011 to 2016. The results showed that the presented model with the accuracy of 91. 5% can detect fraudulent companies.

This paper attempts to evaluate the performance of machine learning models in fraudulent financial Reporting schemes prediction via a multi-classification approach and using an unbalanced dataset. Therefore, the financial statements of 134 companies listed on the Tehran Stock Exchange from 2009 to 2021 were investigated by Logistic Regression, Decision Tree, Boosting Algorithms, and Support Vector Machine. Models were programmed with Python and Performance indicators were calculated and compared. Furthermore, the machine learning model’s performance was investigated in binary classification with the balanced dataset to predict each fraud scheme exclusively. According to the results via a multi-classification approach, then the significant difference between machine learning models’ performance was approved. Support Vector Machin was preferred in multiclass problem space with the unbalanced data set. To predict fraud schemes via binary classification, a significant difference between machine learning models’ performance was not approved except to predict the “Overstatement assets and income” scheme. Support Vector Machin was preferred to Logistic Regression and Decision Tree model. The present research attempts to fill the research gap in the research area by developing machine learning models with a multi-classification approach.

AbstractTax is an expense that imposed by the government on all for-profit units that generate revenue in some way. Tax strategy is generally defined as an obvious tax reduction. Managers strategically use corporate disclosures to mislead or influence investors' perceptions of corporate value. The purpose of this study is to investigate the effect of Tax aggressiveness and Accounting Fraud on Financial Reporting Readability in firms listed in Tehran Stock Exchange. Based on this, the information required for the research was extracted from 87 firms listed in Tehran Stock Exchange during the years 2010-2018. In this study, multivariate linear regression model has been used to test the hypotheses. Findings indicate that Tax aggressiveness using the effective tax rate index does not affect the readability of financial reporting. But Tax aggressiveness using the tax shelter index has a significant negative impact on the readability of financial reporting. Also, accounting fraud has a negative and significant effect on the readability of financial reporting.

The possibility of fraud in the issued financial statements, and its negative impacts on financial markets and the resulting reduction of investment have caused responsible monitoring organizations to detect the frauds and to move seriously against them. This study aimed to investigate the ability of the fuzzy approaches to fraud detection in financial reporting of the firms in the Tehran Stock Exchange. In this study, three hypotheses were considered. First, the Fuzzy decision tree classifier can detect fraud in financial reporting. Secondly, the Sugeno fuzzy classifier can detect fraud in financial reporting. Thirdly, there is a significant difference between the results of fuzzy decision tree classifier and Sugeno fuzzy classifier. These fuzzy approaches were programmed and used for testing the above hypotheses, using Matlab Software. The average accuracy of the Fuzzy decision tree classifier was 31/312, and of the Sugeno fuzzy classifier was 80/92. In other words, the first hypothesis was rejected and the second and third hypotheses were verified.

Purpose: Accounting fraud is a serious misconduct that harms the confidence of investors in the capital market and is one of the controversial topics in the financial field. Many researches have examined the effect of fraud on the capital market from different aspects, but it is still not clear whether the disclosure of frauds committed by companies in the past affects the risk of future stock price crash. Therefore, the purpose of this research is to investigate the relationship between the detection of fraud in financial statements by market participants and the risk of crashing future stock prices.Method: In order to achieve the goal of the research, 143 companies were selected among the companies admitted to the Tehran Stock Exchange during the years 1392 to 1400 by systematic elimination method and a total of 1287 company-years were considered.Results: The findings of the research show that fraud in the financial statements of companies and its detection by market participants has a positive and significant effect on the risk of crashing stock prices.Conclusion: The findings of this research show that following the disclosure of accounting frauds and the accumulation of bad news by the company and the deterioration of the quality of the disclosure and the vagueness of the information environment of the company, the trust of the investors has been lost and in turn lead to It is the risk of crashing stock prices. This supports our theoretical mechanism that accounting fraud affects stock price crash risk through information opacity.Contribution: This research can help investors to investigate the information environment of companies more deeply and understand the power of information environment of companies in their financial decisions.