Detecting Financial Statement Frauds in Malaysia: Comparing the Abilities of Beneish and Dechow Models

Abstract

Financial statement frauds (FSF) are becoming rampant phenomena in current economic and financial landscapes. One of the ways to curb FSF is to detect them early so that preventive measures can be applied. This study aims to empirically investigate the abilities of two financial-based models namely the Beneish's M-score and Dechow's F-score, to detect and predict FSF for Malaysian companies. In addition, this study compares the accuracy including the error rates between the two models. Financial data of Malaysian listed companies from 2001 to 2014 are used using a matched pair in this study. The findings reveal that both Beneish and Dechow models are effective in predicting both the fraudulent and non-fraudulent companies with average accuracy at 73.17% and 76.22%, respectively. The results also indicate that Dechow F-score model outperforms the Beneish M-score model in the sensitivity of predicting fraud cases with 73.17% compared to 69.51%. On the efficiency aspect, the Dechow F Score model is found to have lower type II error (26.83%) than Beneish M Score model (30.49%). This finding suggests that Dechow F Score model is a better model that can be used by the regulators to detect FSF among companies in Malaysia.