Fraud is normal penman in business main object of this paper, is investigation data mining & financial fraud based on financial ratios.The results paper indicate that, data mining technique for financial fraud is suitable. Also, this technique as core of manager think for business management at fraud.

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.

In this paper, the fraud detection model in financial statements is examined by using the financial ratios tool. The purpose of the fraud detection model is to examine the financial statements using the financial instrumnet tool. The period of this research is considered to be 1339-1394. The statistical population includes all companies accepted in Tehran Stock Exchange. The statistical sample consisted of 35 healthy companies with healthy, non fraudulent statements in the Tehran Stock Exchange, which had been listed on the Stock Exchange before 1387, and during this period, at least once in the list of the Top Vocational Companies Declared by the Stock Exchange. Research in terms of the purpose of the theoretical type and in terms of analytical-experimental methodology. In order to investigate the relationship between independent variables and dependent variable, two – way independent t-test and Mann-Whitney test as well as developed logistic regression model were used. The results of the research showed that by using the financial statements analysis of listed companies Securities can be discovered in fraudulent financial reporting. The results of the research indicate that all financial ratios include the ratio of total debt to total assets, the ratio of liquidity to current debt, the financial ratios of current assets to current debt, and the ratio of net profit to equity in determining fraud. Has an effective role.

Financial fraud detection is a challenging problem due to four primary reasons: the constantly changing fraudulent behavior, the lack of a mechanism to track fraud data, the specific limitations of available detection techniques (such as machine learning algorithms), and the highly dispersed financial fraud dataset. Thus, it can be declared that teaching algorithms are complex. The current study used machine learning techniques, including support vector machine regression and boosted regression tree, to detect financial fraud in the Iranian stock market. The findings indicated that the boosted regression tree machine model has the lowest RMSE. Furthermore, concerned with the sensitivity value of the models, the boosted regression tree model has the highest sensitivity in the sense that they had correctly detected the absence of financial fraud Tehran Stock Exchange market the Tehran Stock Exchange market. The boosted regression tree has the highest kappa coefficient indicating the appropriate performance of this model compared to other models used in the research.

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.

In the developing countries the central governments play mainly the major role in society. In these societies the distribution of wealth is not fairly that leads to economic hardship and social exclusion as the base of corruption. For this reason considering the financial, controlling and monitoring indicators are deniable. And it should change the controlling indicators according to economic development in order to fight against financial fraud as one of the economic picky component. In this manner recognition of effective parameters on financial fraud is absolutely essential. In this article by utilization of the related studies and interviewing with experts, the effective parameters on financial fraud were identified. Afterwards by using of collected data from 388 questionnaires besides Topsis fuzzy approach, these parameters were prioritized. According to the results the main causes of this problem are: the dependence of central bank, lack of efficient controlling and monitoring system besides other causes, which have been presented by a preventive methods and suggestions.

Purpose: The purpose of this study is to examine the evolution of financial fraud theories and presentation of the meta-model of fraud, which explains the financial and non-financial incentives for committing fraud in the form of different models. The problem of research includes the followings: What are the most important motives for committing fraud? Which fraud model can explain the motives for committing fraud in more detail? And what are the results of these models for accountants? Methodology: This research uses content analysis and meta-analysis techniques for published researches related to fraud in time period of 1950-2017. Findings: The findings of the study showed that important fraud model in the 1950s began with "fraud triangle ", which included three dimensions of pressure, opportunity and justification, and the financial problems, the weakness of internal control system, and the unfavorable situation of work were identified as a fraud motive. In the 1980s, personal characteristics and the work environment were identified, and then "person integrity" was introduced. In the 1990s, behavioral factors, especially reputation, subjective norms, and control over tendency, were identified as the motivating factor for managers. In the 2000s, the "individual capabilities" including intelligence, the position, and role of the individual were identified, and cultural and social dimensions were considered. Since 2010, there has been consideration to meta-models. Conclusion: The provided mete-model of this study includes financial and nonfinancial dimensions and is divided into four general categories of conditions, situational, normative, and human intelligence; each of them can be analyzed in three levels: individual, organizational, and international. Contribution: In this study, the components of fraud triangle and its four components (motivation / pressure, opportunity, orientation / justification and capability) are studied simultaneously and based on it, a comprehensive metamodel was presented.

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.

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%.