جستجوی مقالات مرتبط با کلیدواژه "تقلب صورت های مالی" در نشریات گروه "حسابداری"

 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.

This paper aims the detection of financial statements fraud according to audit report of financial statments. The initial research data were collected from a statistical sample consisting of 164 companies, listed in the Tehran Stock Exchange from 2014 to 2017 and selected through the systematic sampling method. The statistical sample was divided into two separate groups, i.e. fraudulent (1) and non-fraudulent (0) companies. The independent fraud-related variables included 41 financial and nonfinancial variables, selected through theoretical foundations and the research background. The data of variables, collected through the desk method, were finally analyzed through the top five techniques of machine learning, including; the Bayesian networks, the decision tree, artificial neural networks, support vector machine, and combined method. According to the results, all of these techniques were highly capable of fraud detection in financial statements. Moreover, the proposed combined technique outperformed the other techniques in evaluation accuracy and power with an estimation rate of 96.2%.

The overall purpose of publishing financial statements is to provide information about financial status, performance results, and cash flows to stakeholders. Users' trust, especially investors, shareholders, and creditors, with the information in these statements is an incentive that leads one to fraud in financial reporting. The purpose of present research is to predict fraud in fraudulent financial statements fraud. This triangle is based on the assumption that one is motivated to commit fraud when there are three elements of fraud. These three elements are 1. some perceived motivations for fraud, 2. some opportunities for fraud, and 3. methods of reasoning that fraud does not harm the value of the perpetrator (Cressey, 1973). In this study, the dependent variable of financial statement fraud is used by the researcher as a substitute for earnings management (profit). Independent variables include the pressure of financial stability, the greed of the perpetrator, ineffective supervision, effective supervision, the pressure of external expectations, and predicted financial goals. The statistical population of the study is listed companies in Tehran Stock Exchange and the statistical sample of 98 companies is selected through systematic elimination method during the years 2012 - 2018. The results of testing the research hypotheses using multivariate regression model and panel data model showed that the pressure of financial stability has a significant relationship with financial statements Fraud.