Predicting fraudulent financial reporting using artificial neural network
| Pages | 362-387 |
| DOI | https://doi.org/10.1108/JFC-11-2015-0061 |
| Date | 02 May 2017 |
| Published date | 02 May 2017 |
| Author | Normah Omar,Zulaikha ‘Amirah Johari,Malcolm Smith |
| Subject Matter | Accounting & Finance,Financial risk/company failure,Financial crime |
Predicting fraudulent nancial
reporting using
articial neural network
Normah Omar and Zulaikha ‘Amirah Johari
Accounting Research Institute, Universiti Teknologi MARA, Shah Alam,
Malaysia, and
Malcolm Smith
College of Business, University of Derby, Derby, UK
Abstract
Purpose –This paper aims to explore the effectiveness of an articial neural network (ANN) in predicting
fraudulent nancial reporting in small market capitalization companies in Malaysia.
Design/methodology/approach –Based on the concepts of ANN, a mathematical model was developed
to compare non-fraud and fraud companies selected from among small market capitalization companies in
Malaysia; the fraud companies had already been charged by the Securities Commission for falsication of
nancial statements. Ten nancial ratios are used as fraud risk indicators to predict fraudulent nancial
reporting using ANN.
Findings –The ndings indicate that the proposed ANN methodology outperforms other statistical
techniques widely used for predicting fraudulent nancial reporting.
Originality/value –The study is one of few to adopt the ANN approach for the prediction of nancial
reporting fraud.
Keywords ANN, Fraud prediction models, Small market capitalization companies
Paper type Research paper
1. Introduction
During the past decade, issues of fraudulent nancial reporting seem to be one of the hottest
topics raised by regulators and enforcement agencies globally. Fraudulent nancial
reporting involves the manipulation of nancial accounts by overstating assets, revenue and
prot or understating liabilities, expenses or losses. These issues are bothersome to the
investors, creditors and the public, as a whole, because of the huge impact on all of them.
Employees lose their jobs, investors do not get optimal returns on their investments,
creditors are unable to get their payments and, as a result, the public lose their faith in the
legislation. Meanwhile, companies as well as professionals such as the auditors and
accountants need to be more vigilant to mitigate and combat nancial fraud.
Increasing fraudulent nancial statements among public companies in the past decade
has focused public attention on the process of preparing nancial statements. Research done
by KPMG found that about 78 per cent shows that the number of fraud cases among
companies is expected to arise due to the current nancial crisis (KPMG, 2009). As a
The research was supported by Accounting Research Institute (ARI) and the Faculty of Accountancy
within Universiti Teknologi Mara, Malaysia.
This research was supported by HICoE grant, Ministry of Education, Malaysia.
The current issue and full text archive of this journal is available on Emerald Insight at:
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JFC
24,2
362
Journalof Financial Crime
Vol.24 No. 2, 2017
pp.362-387
©Emerald Publishing Limited
1359-0790
DOI 10.1108/JFC-11-2015-0061
consequence, the fraudulent nancial statements can corrode public condence on the
reliability of nancial reporting as a means to assess a rm’s future prospects.
Financial frauds, unlike other hard crimes such as murder and rape, are harder to prove
in the court of law. A study by the Association of Certied Fraud Examiners (ACFE) (2012)
concluded that nancial frauds normally take between three and six years to detect. Usually,
by the time nancial frauds are detected, their related evidences have either been removed or
distorted. There are also no scientic tests, such as DNA or ngerprint, that can be
conducted. Just like any other serious illnesses, fraud is best “prevented than cured”.
Due to its seriousness, many fraud detection methods had been especially implemented to
help the auditors to detect, or better yet, to predict, fraudulent nancial reporting because
they are the rst layer of defence that could prevent fraud activity from spreading. Some of
the most commonly used methods of analysis are trend analysis, common-sized nancial
statement analysis, nancial ratio, Beneish model and Altman Z-score. Of late, researchers
have also proposed the use of data mining techniques to predict the occurrence of fraud in
nancial statements.
Data mining is a data processing tool that uses sophisticated data search capabilities and
statistical algorithms to discover patterns and correlations in large data. Some of the
proposed data mining algorithms that can be used to predict fraudulent nancial reporting
are logistic regression, probit regression, decision trees and Bayesian networks and articial
neural networks (ANNs) (Kirkos et al., 2007;Kotsiantis et al., 2006;Liou, 2008). Nevertheless,
the use of these newer techniques is either limited or not adequately reported and
documented in literature.
Premised on the purported advantages of the use of these techniques, this study explores
the use of ANNs to predict fraudulent nancial reporting in small market capitalization
companies in Malaysia. This study focuses on fraudulent nancial statements, which is also
known as management fraud (Kranacher et al., 2011;Zimbelman and Albrecht, 2012).
Management fraud involves top management’s deceptive manipulation of nancial
statements. The reason the top management commits the fraud is to make the company look
better than it is. This is the most expensive type of fraud. The common examples of
management fraud are Enron, WorldCom, Waste Management, Parmalat and Satyam,
among others. After examining two sample types, namely, fraud companies which were
charged by the Securities Commission for presenting falsied nancial statements and
matched with non-fraud companies which were selected among small market cap
companies, this study found that ANN could give a higher prediction result (94.87 per cent)
on fraudulent nancial reporting model compared to traditional statistic, linear regression
(92.4 per cent) and other techniques as in previous study.
Previous studies have focused mainly on large capitalization companies (Abbott and
Parker, 2000;Klein, 2002;Lin and Hwang, 2010;Lin et al., 2006), with little attention having
been paid to the study of mid-cap and small-cap companies (Kang et al., 2011). Regulators,
analysts, investors and the public have continued to focus their attention on big companies
because of the effect of failure of big companies towards them (Arnott and Wu, 2012),
meaning that lower levels of scrutiny provide the opportunity for the small-cap companies to
commit fraud. Thus, it is crucial to study on small market-cap companies.
This study is signicant in the Malaysian context, as there is limited study on adapting
ANN in predicting fraudulent nancial reporting, especially using small market cap as the
sample. Nevertheless, there is limited exploration on specically using multilayer feed
forward neural network (MLF) in the Malaysian accounting eld, especially in detecting and
predicting fraudulent nancial reporting. Instead of helping in extending the literature on
both ANN and small market-cap companies as well as the practitioners, this study is also
363
Articial
neural network
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