Fraudulent Financial Statement Detection Using Statistical Techniques: The Case of Small Medium Automotive Enterprise

Abstract

Fraudulent financial statements (FFS) are now placed under greater public scrutiny following an increase in the number of collapses among companies due to management fraud with loses on average at 5% of revenue (ACFE, 2014). There is consensus that management fraud is an on-going reality and no single organization is immune from the damage caused by the fraudsters (KPMG Malaysia, 2009). Small and medium sized businesses are also threatened by fraudulent activities and statistics showed organizations with fewer than 100 employees experienced more fraud cases than larger corporations (ACFE, 2008). Most of the companies in the automotive industry in Malaysia are small and medium scaled, hence these companies bear a greater burden and face higher risks of fraud. Precautionary measures in preventing fraud are crucial; however, with limited resources, effective detection may be severely curtailed. This paper assesses the possibility of FFS in a small medium automotive company in Malaysia using three statistical analyses namely the Beneish model, Altman Z-Score and Financial Ratio. The findings show that there are riskier zones that need to be further investigated by the management. It is suggested for the company to establish an internal audit unit to provide assurance on the company’s operations, financial reporting accuracy and adherence to the regulations.