Towards a Machine Learning Approach for Earnings Manipulation Detection

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Bilal Dbouk
Iyad Zaarour


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


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