Detecting Financial Statement Fraud by Malaysian Public Listed Companies: The Reliability of the Beneish M-Score Model

Abstract

Various fraud prediction tools have been developed to detect financial statement fraud triggered by earnings manipulation. Among them is the Beneish M-Score model as a financial forensic tool to gauge potential earnings manipulation in firms' financial statements. The model was found to be effective in detecting 76% of earnings manipulating firms subjected to accounting enforcement actions by the United States Securities and Exchange Commission (U.S. SEC). Furthermore, the earnings manipulation model was also successful in discovering 71% of the most prominent fraudulent financial reporting scandals in the United States prior to public announcement. The current study assesses the reliability of the Beneish M-Score model in detecting earnings manipulation and financial statement fraud committed by Malaysian public listed companies prior to public announcement. The research sample consists of 17 public listed companies of which their directors and top management have been charged and prosecuted by the Securities Commission Malaysia (SC) for committing fraudulent reporting and misstatement from 1996 until 2014. The results show that the Beneish M-score model is reliable in detecting earnings manipulation and financial statement fraud by 82% in 14 out of 17 listed companies charged for fraudulent financial reporting. The findings provide support for the application of the Beneish M-Score model by the management of the company to check for any irregularities in firms' financial report so that adjustment can be made before submission to Bursa Malaysia to prevent from any potential backlash that could damage firm reputation. The model application is likewise beneficial to prospective and existing shareholders to assist their investment decision making in reducing risk of losses due to fraud. In addition, the findings suggest that the model can be applied by researchers, auditors, and enforcement agencies as an effective detection tool to signal potentially fraudulent reporting companies in Bursa Malaysia for further investigation and enforcement action.

Integrated thinking, earnings manipulation and value creation: Malaysian empirical evidence

“…M -score model requires two years of organisations' financial data to calculate the organisations' tendency to engage in earnings manipulation (Beneish, 1999; Aris et al , 2013; Shanmugam et al , 2003). This model is also used to identify the manipulators and non-likely manipulator organisations with the threshold limit of −2.22 (Ahmed and Naima, 2016; Beneish, 1999; Kamal et al , 2016; Özcan, 2018; Petrík, 2016). The organisation is classified as a manipulator when the score is above −2.22 and a non-likely manipulator when the score value is below the threshold limit.…”

Financial Fraud Identification Based on Stacking Ensemble Learning Algorithm: Introducing MD&A Text Information

“…Early on, some scholars used the M-Score [17][18][19], F-Score [20], and Z-Score [21] models to evaluate the possibility of financial fraud. Some scholars have also applied Benford's law to the identification of financial fraud in the accounting field based on the distribution law of the first digit in the dataset and verified its applicability [22].…”

The Relationship Between Perceived Pressure, Perceived Opportunity, Perceived Rationalization and Fraud Tendency Among Employees: A Study from the People’s Trust in Malaysia

“…As reported by (Liew, 2008), several factors influence corporate fraud factors, such as political nepotism, cronyism and corruption; low regulatory structure to protect shareholders and investors; lack of accountability and inadequate disclosure of information, and inadequate audits and risk management. Yet, fraud cases frequently taking place in Malaysia, but the study is not well reported and well-undocumented (Jaffar, Ismail, & Hway Boon, 2011;Kamal, Salleh, & Ahmad, 2016), due to retaliatory effect by employers as suggested by (Aisyah Basri, Daud Marsam, Majid, Abu, & Mohamed, 2017;Alford, 2016;Guthrie & Taylor, 2017;Kanojia, Sachdeva, & Sharma, 2020). From the given evidence, research on corporate fraud is essential to fill in Malaysia's loopholes since there is a lack of research studies in this area and to mitigate the upward fraud trend.…”