FRAUDULENT FINANCIAL REPORTING DETECTION USING BENEISH M-SCORE MODEL IN PUBLIC COMPANIES IN 2012-2016
Keywords:
Fraudulent financial reporting, Beneish M-Score, Logistic RegressionAbstract
This study aims to examine the ability of the Beneish M-Score model to detect fraudulent financial reporting. The Data research sample consisted of 55 manipulator companies that were sanctioned and fined by the Financial Services Authority due to violating POJK number X.K.1; X.K.4; IX.E.1; IX.E.2 and VIII.G.7 during 2012-2016 and 55 non-manipulator companies as comparison companies. The results showed that 28 of the 55 manipulator companies were correctly classified as fraud firms by 50.91%, while for the category of non-manipulator companies, there were as many as 60% or 33 of the 55 companies correctly classified as non-fraud firms.The results also identified three M-Score variables that were often manipulated by manipulator and non-manipulator companies namely Sales and General and Administrative Expenses Index (SGAI), Depreciation Index (DEPI) and Asset Quality Index (AQI), but two other variables namely Days Sales in Receivables Index (DSRI) and Sales Growth Index (SGI) are considered unable to detect fraudulent financial reporting. In addition, based on the results of the logistic regression test, the eight Beneish M-Score variables have no effect on the detection of fraudulent financial reporting. The Beneish M-Score model is an easy and cheap way to detect possible fraudulent financial reporting, this can be applied by investors before determining their investment decisions.References
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