Providing a Model for Forecasting fraudulent Financial Statements and Comparing Financial Statements and Ratios with Benford Law

The purpose of the present study is to provide a model for predicting fraudulent financial statements and applying Benford law to listed companies in Tehran Stock Exchange. The research method is descriptive-survey and in terms of purpose, it is also applicable. The research data were collected from 2008-2018. The statistical sample of the study consisted of 410 years-company of fraudulent companies and 410 years-company of non-fraudulent companies. Logistic regression method was used to develop the model. The results show that given the accuracy rate of 64.6%, this model plays an effective role in detecting financial statement fraud. The results of T-test and Levon test in 35 independent variables showed that in 20 variables, there was a significant difference between the fraudulent and non fraudulent groups. In addition, the compliance and any possible manipulations of the Benford law were examined in four different cases, And the results are as follows: A look at the income statement figures and balance sheets in non-fraudulent companies showed that the Banford distribution correctly identified the fraudulent companies but identified the fraudulent companies as inaccurate. Consideration of the financial ratio of total assets to sales and the Days payable outstanding to fraudulent companies showed that the Bannford distribution classified these companies as fraudulent and correctly classified them, but assessed the non-fraudulent companies as fraudulent