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.

The main purpose of this study investigates the effect of audit quality in the financial statement fraud. This study investigates whether the risk of fraud in the financial statements of companies with higher audit quality has been reduced. In general, the higher audit quality in reducing the incidence of in the financial statements fraud of companies listed in Tehran Stock Exchange (48 companies' fraud and 48 companies' non- fraud) the period 2008 to 2014 were reviewed. To test the hypotheses, research logistic regressions is used. results show a significant negative relationship between the audit firm size, audit industry specialization, the length of the auditor -client relationship, industry specialistaudit firms with long tenure and quality control point with the financial statement fraud which indicates that the Whatever, the higher the quality audit firms committing fraud in the financial statements less.

The present study is attempts investigate the impact of financial statement comparability on corporate financial cash holdings. To this end, three various proxies based on DeFranco et al’ s (2011) were employed to evaluate financial statement comparability, and Ozcan & Ozcan’ s (2004) model was also used to measure cash holdings. The research hypothesis was also developed based on the data collected form a sample of 82 firms listed on the Tehran Stock Exchange during the years 2013-2017, and then tested using multivariate regression model based on panel data. The results indicate that financial statement comparability reduces the level of corporate cash holdings. This finding means that, financial statement comparability can mitigate uncertainty and facilitate the monitoring of the evaluation of the managerial performance through attenuating acquisition costs and enhancing the quality and quantity of the information available to investors. Accordingly, firms with comparable financial statements confront with less external financing costs and restrictions, thereby less likely to hold cash. Moreover, these results are robust and are not sensitive with respect to alternative measure of cash holdings and individual analysis of the research hypothesis for each year.

Financial statement comparability improves the quality of financial information and the information environment, and enabling users to identify similarities and differences between different companies, and evaluating the performance of managers and supervising them. So, it is expected that increasing the comparability of financial statements will limit the opportunism of managers. In this regard, in this study, the relationship between comparability of companies and debt maturity has been investigated. The data of the present study were collected using the financial information of 125 companies listed on the Tehran Stock Exchange in the period 2013 to 2019 (882 observation). To analyze the data, a multivariate linear regression model of the generalized least squares type by utilizing combined data was used. The results showed that there is a negative and significant relationship between the comparability of financial statements and the maturity of the company's debt. Therefore, it can be concluded that the Financial statement comparability plays an important role in aligning incentives in the company and by reducing information asymmetry and potential agency costs, can substitute for the use of short-term debt by serving as a corporate governance mechanism

Bankruptcy and its effects on investors, wealth and general economy of the country is one of important financial research topics. So researchers try to identify items affecting the bankruptcy. The purpose of this study is to investigate the relation of liquidity and corporate life cycle to bankruptcy and the effect of corporate life cycle stages on the relation between liquidity and bankruptcy in companies listed in Tehran Stock Exchange. Therefore, three hypotheses are designed and tested using the data from a sample consisting of companies listed in Tehran Stock Exchange during the period 2012 to 2019. The results of logistic regression analysis indicate a significant negative relation of liquidity and corporate life cycle to bankruptcy. However, corporate life cycle has no significant effect on the relation between liquidity and bankruptcy.

The purpose of this study is to compare neural network, decision tree, nearest neighbor and support vector machine data mining techniques in predicting fraudulent and non-fraudulent financial statements. The research method is descriptive-applied and time domain from 2008 to 2018. In this study, financial ratios for two fraudulent and non-fraudulent samples and data mining methods were analyzed. Statistical hypotheses of normality, homogeneity and linearity test for financial ratios of fraudulent and non-fraudulent samples were tested. The normality hypothesis was tested using Kolmogorov-Smirnov test and Shapiro Wilk test. Then Pearson correlation coefficient for the existence of the model for financial ratios and elimination of correlated independent variables was tested. Next, data mining methods are used to test them in predicting financial statement fraud and distinguishing fraudulent and non-fraudulent financial statements. In general, the results show that data mining methods are effective in differentiating fraudulent and non-fraudulent financial statements. The neural network method had a correct prediction of 69.4%, decision tree 65.4%, nearest neighbor 64.4% and support vector machine 78%.

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.