Towards a Machine Learning Approach for Earnings Manipulation Detection - Dbouk, Bilal; Zaarour, Iyad (Asian Journal of Business and Accounting, 2017)
Manuscript type: Research paper. Research aim: This paper aims to enhance Earnings Manipulation Detection (EMD) by applying the Bayesian Naïve Classifier (BNC), a supervised machine learning approach which evaluates and compares the manual auditors’ method with a widely applied mathematical model (Beneish model). Design/ Methodology/ Approach: The data set consists of financial statements of 53 companies over years 2006 to 2009. Three data sets were created: training data set using financial statements from 2006 to 2007, and two test data sets made up of financial statements from 2008 to 2009. The Beneish model and the manual auditors’ method are used to test for EMD accuracy as well as to evaluate results. In the process of testing and comparing the two methods, a new layer of supervised machine learning technique namely the BNC is introduced. Research findings: The analysis of results for the EMD shows that the Beneish model outperforms the manual auditors’ method. The results also reveal a higher classification rate (86.84 per cent) when using the Beneish model as compared to the manual auditors’ method (60.53 per cent). This difference indicates that the manual auditors’ method is less effective in detecting earnings manipulation. Theoretical contribution/ Originality: The main contribution of this research is the introduction of the supervised machine learning approach as a new layer in the framework of EMD. This approach can be used to broaden the scope for auditors, forensic accountants, tax controllers, and other manipulation detectors who are involved in the auditing procedures. Practitioner/ Policy implication: The results of this study will help regulators and practitioners to re-define their overall strategic decision-making in detecting accounting manipulations. Research limitation: This study devised a framework for EMD using the Beneish model and machine learning approach for companies operating in wholesale liquid fuel industry. Further studies need to be conducted to examine the application of such methods in other industries. In addition, future studies may need to assess the potential of other manipulation detection models which could be applied together with different machine learning tools. Keywords: Beneish M-Score, Earnings Manipulation, Machine Learning, Supervised Classification JEL Classification: C1, M4