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.

In this study, the ability of artificial neural networks (ANN) as a novel method for Predicting the Likelihood of Fraudulent financial reporting of listed companies in Tehran Stock Exchange in a period of 9 years between the years 2006 to 2015 were studied. For this purpose, the information contained in the financial statements and financial ratios and Multilayer Perceptron model, which includes an input layer, hidden layer of visibility software MATLAB, and an output layer is, the Likelihood of distorted presentation of the financial report of Fraudulent financial reporting through techniques neural network was evaluated. In this regard, the first seven years of information companies, to develop and train the neural network, data validation and verification of the eighth to the ninth year of training, networking and data as test data and test network were designed. Finally, with regard to the results, it was found that the neural network modeling techniques based on neural network integrity is 97. 4% and the design and rigorous training, neural networks can be designed with reasonable accuracy the probability to detect and predict Fraudulent financial reporting companies.

The main purpose of this article is to predict Fraudulent financial statements using the CRISP approach. The preliminary data analyzed in this study are from the statistical sample of 164 companies admitted to Tehran Stock Exchange during the period of 2015-2018, which were selected by systematic elimination sampling. The independent variables affecting Fraud in this study included 40 financial and non-financial variables that were selected based on antecedent research. Finally, data on variables collected by the library method, based on Crisp approach, to determine the weight and specificity of important variables to the Shannon entropy model and to predict cheating in the top four techniques among intelligence techniques. These techniques include 2 decision trees, neural networks, support vector machines, and the adiabatic hybrid backup vector machine. Using the Shannon entropy out of the 40 research variables, the top 27 variables were identified based on the information profit attribute, which identified the variable cumulative earnings-to-sales ratio as the most important variable in Predicting financial statement Fraud. After applying the Crisp approach, the results showed that all techniques were capable of detecting financial statements at a relatively high level, and the proposed technique of Adaptive Backup Vector Machine in the training phase with an accuracy rate of 81. 69% had higher accuracy and evaluation ability than the other techniques. And this technique correctly identified 82% of Fraudulent and non-Fraudulent financial statements in the year 2018.

Fraud is a common phenomenon in business, and according to Section 24 of the Iranian Auditing Standards, it is the Fraudulent act of one or more managers, employees, or third parties to derive unfair advantage and any intentional or unlawful conduct. Financial statements are a means of transmitting confidential management information about the financial position of a company to shareholders and other stakeholders. In this paper, by reviewing the literature, 6 indicators of current ratio, debt ratio, inventory turnover ratio, sales growth index, total asset turnover ratio, and capital return ratio as input and detection of financial Fraud as output are considered for the fuzzy neural network. The database was compiled for 10 companies in the period from 2010 to 2018 after clearing and normalizing qualitatively between 1 to 5 discrete numbers with very low or very high meanings, respectively. The fuzzy neural network model with 161 nodes, 448 linear parameters, 36 nonlinear parameters, and 64 fuzzy laws with two methods of accuracy approximation of mean squared error and root mean squared error has been set to zero and 0. 0000001 respectively. This neural network can be used for prediction.

Background: Management responsibility is creating the right organizational climate in which Fraud is the worst crime. methods of identifying Fraud play an important role in preventing Fraud. Objective: To provide financial policy to management in Predicting financial Fraud by using neural network data mining Research Method: Descriptive-applied research method and time domain is also from 2008 to 2017. In this study, financial ratios for both Fraudulent and non-Fraudulent samples and network data mining were analyzed. Pearson's correlation coefficient was then examined for the model linearity for financial ratios and the elimination of independent correlated variables. In the next step, the neural network method was used to provide financial policy to management regarding the prediction of financial statement Fraud. Findings: The decision tree method is effective in providing financial policy to management in Predicting financial statement Fraud. Conclusion: Since the decision tree method has 65. 4% correct forecast, it can be effective in providing financial policy to management to predict Fraud.

Professional skepticism is an attitude that includes a questioning mind that makes to be alert to conditions indicating Fraud, management bias, and inconsistencies across evidence. An appropriate level of professional skepticism is essential to a high-quality audit, for this purpose, this paper aims to investigate the effect of partner communication of Fraud Likelihood and skeptical orientation on the level of professional skepticism in auditor judgments. This study examines the effect on professional skepticism of the partner’s communication on the Likelihood of Fraud (making their own view known, making management’s view known or not making any view known) and the skeptical orientation being encouraged (outward orientation towards the veracity of management representations and/or inward towards the fallibility of the auditor’s judgment processes). The statistical sample consisted of 185 auditors working in trusted audit institutions of Tehran Securities & Exchange Organization in 2023 who were selected by convenience sampling. To investigate hypotheses and analyze data, an analysis of variance and the SPSS software were used. Results indicate that auditors exhibit higher levels of professional skepticism when the partner expresses management’s view, rather than their own view or no view, that there is a low Likelihood of Fraud. Also finding indicates emphasizing an inward skeptical orientation was not found to be more effective in encouraging professional skepticism in audit judgments than emphasizing an outward skeptical orientation.

The accuracy of financial information disclosed by companies is the main source of investor information for making investment decisions. Therefore, any Fraud in the submission of financial statements leads to unfavorable management of financial resources and non-optimal investment decisions. Empirical studies of financial reporting Fraud predictions have focused on firm-level variables and neglected business climate variables. Therefore, in this study, the role of business environment on the probability of Fraud in the financial statements of 120 companies listed on the stock exchange and securities during the period 2012-2019 has been investigated using a logistic regression approach. The results showed that the estimated logit pattern explains about 25.59% of the changes in the ratio of the probability of Fraud in financial statements to its non-occurrence. Also, among the 9 variables of business environment, two variables of per capita income and free market exchange rate have a significant effect on the probability of Fraud in companies' financial reports, and with a one percent increase in per capita income, the probability of cheating in financial reports is 0.5232% With the decrease and increase of the exchange rate in the open market, the probability of Fraud in the financial reports of the surveyed companies increases by 0.4338%. These results indicate the importance of business environment variables along with company level variables in Predicting the Likelihood of Fraud in corporate financial statements.

The current era is known as the "age of information," and the capital market is built on information as the economy's primary engine. The system of financial statements of corporations, which is the most significant source of information used in the capital market, produces an information system called accounting. Fraud and manipulation in these financial statements raise corporate risk, erode investor confidence, and cast doubt on the objectivity of accounting experts. Owing to the significance of Fraud, this study aims to offer a way to foretell the Likelihood of Fraud in the financial statements of businesses admitted to the Tehran Stock Exchange between 2014 and 2021. 180 enterprises listed on the stock exchange make up the statistical sample (532 years of companies - suspected Fraud years and 908 years - of non-Fraudulent companies). According to the independent auditor's assessment, the existence of dormant assets and items, the doubting of the assumption of continuity of activity, the presence of tax discrepancies with other tax areas, and the dearth of adequate performance tax reserves led to the selection of the companies suspected of Fraud. 96 financial ratios have been compiled by examining the theoretical foundations and research. In this research, the supervised methods of support vector machine, K-nearest neighbor, Bayesian network, neural network, decision tree, logistic regression, random forest and the hybrid method (bagging) have been used. The results of the research showed that the performance evaluation criteria of precision, accuracy, sensitivity, and F-Measure and efficiency (ROC) and the accuracy result of the confusion matrix in the combined method (bagging) were 72.45, 61.21, 64.74, 62.93, 73.50, and 72.45 percent, respectively, which indicates the better performance and greater ability of this method to predict the possibility of Fraud in financial statements compared to other proposed methods.

AbstractFinancial statement Fraud has become a serious problem for market participants and policy makers. In fact, it threatens the reliability of capital markets, corporate executives and even the auditing profession. The purpose of this study is to use the approach of dynamic averaging models to predict Fraud in financial statements.The present research is applied in terms of method. The research period is 1390 to 1399 and in estimating the model, the data of selected companies in Tehran Stock Exchange has been used.Using the systematic elimination approach, the research sample size of 125 companies was selected. To estimate the model, MATLAB 2021 software has been used.In this research, based on the dynamic averaging model, we predicted the Fraud and accuracy of the estimation models. Based on the results of asset return variables; Return on equity; Operating profit margin; Asset turnover ratio and operating cash-to-sales ratio have a negative effect on Fraud and other variables have a positive effect.

Objective: Considering complex financial plans to conceal Fraud in financial statements, the development of Fraud detection methods can be regarded as solution for this problem. The present study uses the bee algorithm to develop methods for Fraud detection in financial statements. Method: Three methods of bee algorithm, genetic algorithm and logistic regression have been used to study the subject. The statistical sample consists of 120 companies accepted in the Tehran Stock Exchange (60 companies are suspected of Fraud and 60 ones are not suspected) for the period 1396-1385. The companies were suspected of Fraud, based on 1) revised audit opinion after unacceptable expression, 2) existence of significant annual revisions, and revised financial statements for inventories and other assets; 3) existence of tax disputes with the tax area, according to notes on income tax filing, general tax filings and conditioned clauses in audit reports. Following the use of cross-entropy, 16 financial ratios were introduced as the potential predictors of Fraudulent financial reporting. Result: The results showed that the bee algorithm method with prediction accuracy of 82. 5% has better performance in identifying suspicious companies in Fraudulent financial statements than the other two methods. Conclusion: The results of the research indicate that the proposed method of this study compared to other methods has higher rate of prediction accuracy, lower error rate and relatively good speed.