جستجوی مقالات مرتبط با کلیدواژه « گزارشگری مالی متقلبانه » در نشریات گروه « اقتصاد »

The purpose of this study is to determine the importance and ranking of internal and external factors affecting fraudulent financial reporting. The statistical population in this study includes members of the Iranian Society of Certified Public Accountants who have characteristics such as teaching experience at the university, having a doctorate, history of professional activity in the profession of auditing and financial management, which according to the restrictions 30 people were considered as a sample. In this research, AHP technique and Expert Choice software have been used to analyze the data. The results of the analysis showed that extra-organizational factors were the first priority in creating fraudulent reporting with a score of 0.243 and intra-organizational factors were the next priority with a score of 0.214. Also, according to the research results, from external factors, respectively, factors related to the characteristics of independent auditing, cultural factors, legal and regulatory factors outside the organization, and from internal factors, respectively, professional characteristics and management structure. Behavioral characteristics of management, behavioral and moral characteristics, characteristics related to systemic and human errors have been the most important.

People face moral dilemmas in their lives. Managers, accountants, and auditors also face a variety of situations in their careers, situations that tempt them to engage in unethical behavior. One of these unethical behaviors is fraud and earnings manipulation, which leads to ethical failure in companies. Given the potential for financial statement fraud and its detrimental effects on the economy, preventing and detecting financial fraud is important to prevent its disastrous consequences. Therefore, the introduction of an early detection tool is essential to alert executive bodies to further investigation or legal action. Developing a model through which to predict earnings manipulation and detect the unethical behavior of managers, allows for a better evaluation of the company's performance. The purpose of this study is to investigate the ability of Beneish and Spathis models to detect fraudulent financial reporting. Then the coefficients of these two models were adjusted using logistic regression and the ability of these two new adjusted models to detect fraudulent financial reporting was investigated. In this regard, the data of 99 manufacturing companies listed on the Tehran Stock Exchange (1089 observations) during the years 2008 – 2018 were reviewed. The results show that the initial model of Beneish and Spathis does not have good power in detecting fraudulent financial reporting, but the adjusted model of Beneish and the adjusted model of Spathis are able to detect fraudulent financial reporting with 83% and 82% accuracy, respectively.

The existence and persistence of fraud in financial statements can have adverse impact on the sustainable development of the capital markets as well as the financial health of companies. Using conventional audit procedures which is applied to prevent and detect fraudulent financial statements, auditors fail to cope with emerging accounting frauds. This can be due to many reasons, such as the lack of the required data mining knowledge, the complexity and infrequency of financial frauds, and the auditors without much experience. Accordingly, due to importance of identifying fraud in capital market, different types of statistical and machine learning based models were examined to establish a rigorous and effective model to detect financial statements fraud in this study. For this purpose, 20 variables in the form of the pentagonal fraud with emphasis on the structure of internal controls (pressure, opportunity, justification, capability, arrogance and internal control structure) were used from 166 manufacturing companies listed on Tehran stock exchange over the period 2009-2018. Based on the statistical indices obtained, machine learning based models exhibited higher predictive ability and accuracy than statistical based models in predicting financial statement fraud. The results also showed that C5, CHAID and C&R decision tree models were highly accurate in prediction of fraudulent datapresented in fnancial statement. Accordingly, the efficacy of combination of CHAID, C5 and C&R decision tree algorithms which had the highest accuracy in prediction of fraudulent financial reporting was examined. The high accuracy of 92.61% of the combination of these algorithms in fraud prediction shows that data mining methods based on machine learning models and especially their combination can be used successfully in fnancial statement fraud prediction.