Objective: Financial statements are of substantial significance to investors and other players in financial markets. Yet, these statements are sometimes misinforming and misleading. Thus, a potentially intricate issue for financial decision-makers is the prediction and detection of fraudulent financial statements. Method: This research investigates the relationship between earnings management and fraudulent financial statements in Firms, listed in Tehran Stock Exchange in order to help identify fraudulent financial statements in the time interval from 2009 to 2016. In other words, the objective of this study is to evaluate the ability of the popular discretionary accrual models to detect fraudulent financial statements. For this mission, 189 companies (21 fraudulent companies and 168 non-fraudulent) are selected as the research sample. The data mining methods employed in this research are Decision Trees (REPTree), Artificial Neural Networks (ANNs), and Bayesian Networks. We evaluate the ability of 7 measures derived from the extant discretionary accruals models to detect the existence of fraudulence. Results: The obtained results indicate that among all data mining methods, the Decision Trees method and among all accruals models, the Modified Jones model with book-to-market ratio have the greatest relationship with fraudulent financial statements.