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Muhasebe ve Vergi Uygulamaları Dergisi
Ankara SMMMO
543
ASSESSING THE FRAUD RISK FACTORS IN THE
FINANCIAL STATEMENTS WITH BENFORD'S LAW*
Dr. Öğr. Üyesi Onur ÖZEVİNa
Hassan YAZDIFARb
ABSTRACT
The aim of this study is research potential fraud risk in financial statements by using some
financial and nonfinancial parameters. For detection of fraud risk used conformity of
financial statements with Benford's law among different groups of companies. Variables to
be met by financial statement liability include sectors, risk groups, size of independent
audit firms, independent auditing obligation and independent membership of directory
board.
To this intent balance sheet and income statements of the companies traded in BIST real
sector for the years 2008-2017, taken as data set. Data set were applied Benford analysis for
measuring conformity of financial statements with Benford’s law. For analyse difference
between groups applied T-Test, ANOVA and TUKEY tests. As a result, investigated
significant difference between company groups and variables were found to affect the fraud
risk in the financial statements. These results have shown impact of different variables on
financial statement as a fraud risk factor. It has expected that these factors are effective in
the financial statement fraud. Company owners, professsional accountants, auditors and tax
authority can use this method for detecting red flags and selecting audit targets.
Keywords: Benford’s Law, Fraud Detection, Financial Statement Analysis, Fraud Risk
Factors.
JEL Codes: M40, M41, M42.
* Makalenin gönderim tarihi: 04.10.2019; Kabul tarihi: 25.06.2020, iThenticate benzerlik oranı %20
a Bolu Abant İzzet Baysal Üniversitesi, Gerede Meskek Yüksekokulu, onurozevin@ibu.edu.tr
ORCID: 0000-0002-1347-5027
b Bournemouth Unicersity, hyazdifar@bournemouth.ac.uk, ORCID: 0000-0003-3023-2534
APA Stili Kaynak Gösterimi:
Özevin, O., Yazdıfar, H. (2020). Assessing the Fraud Risk Factors in The Financial
Statements with Benford's Law. Muhasebe ve Vergi Uygulamaları Dergisi. 13 (3), 543-
569.
Ampirik Araştırma
(Empirical Research)
Muhasebe ve Vergi
Uygulamaları Dergisi
Kasım 2020; 13 (3): 543-569
Muhasebe ve Vergi Uygulamaları Dergisi
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BENFORD YASASI İLE FİNANSAL TABLOLARDAKİ HİLE
RİSKİNİN BELİRLENMESİ
ÖZ
Bu çalışmanın amacı, bazı finansal ve finansal olmayan parametreler yardımıyla finansal
tablolardaki potansiyel hile riskini araştırmaktır. Hile riskinin tespiti için BIST şirketleri
içerisinden Benford yasasına uyum kriterine göre kontrol ve risk grupları belirlenmiştir.
Hile riskinde etkili olabilecek faktörler olarak; sektör, BIST risk grupları, bağımsız denetim
firmalarının büyüklüğü, bağımsız denetim yükümlülüğü ve bağımsız yönetim kurulu üye
sayısı olmak üzere beş değişken seçilmiştir.
Bu amaçla BIST reel sektörde işlem gören şirketlerin 2008-2017 yılları için bilanço ve gelir
tabloları veri seti olarak alınmıştır. Finansal tabloların Benford yasasına uygunluğunu
ölçmek için veri setine Benford analizi uygulanmıştır. T-Testi, ANOVA ve TUKEY testleri
gruplar arasındaki farklılıkarın analiz edilmesinde kullanılmıştır. Sonuç olarak, şirket
grupları arasında hile riski açısından anlamlı farklar belirlenmiş dolayısıyla söz konusu
değişkenlerin finansal tablolarda hile riskini etkilediği tespit edilmiştir. Şirket sahipleri,
muhasebe meslek mensupları, denetçiler ve vergi otoritesi gibi çevrelerin bu yöntemi
kırmızı bayrakları tespit etmek ve denetim hedeflerini seçmek için kullanabileceği
değerlendirilmektedir.
Anahtar Sözcükler: Benford Yasası, Hile Denetimi, Finansal Tablolar Analizi, Hile Risk
Faktörleri.
JEL Kodları: M40, M41, M42.
1. INTRODUCTION
1
The truth of financial information has a direct impact on social and
economic life (Robinson et al., 2015). Therefore, an economic system that is
effective, smoothly functioning capital markets, social and economic
dynamics such as fair taxation structure are directly related to the correct and
honest transfer of financial information (Moller, 2009; Aaker and Jacobson,
1994; Franco et al., 2011; Watrin et al., 2008). Frauds in the accounting
process may cause the other parties to damage and lose by showing the
financial statements differently than they actually are. These losses
negatively affect all economic aspects from individual to government. For
these reasons, accounting audit has been expected to be effective in order to
minimize these negativities. The fact that the financial statement data
reflects reality, being free of mistakes, prejudices or manipulations is very
important for a well-functioning economic system. Accurate financial
reports provide efficient resource allocation and effective investment
(Conceptual Framework for Financial Reporting 2018). Therefore, the
assessment of errors in the financial statements is an important task for
investors, analysts, auditors, regulators and researchers. The research
questions are that; is it possible to detect correctness of financial data with
1
This study was produced from a PhD. dissertation titled “Finansal Tablolarda Hile Riskinin Tespiti
Üzerine Bir Model Önerisi: BIST Uygulaması”.
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Benford’s Law? And which variables can be effective in determining fraud
risk in the financial statements?
As the use of software designed for specific and general audit purposes
becomes widespread, audit coverage expands. Nonetheless, according to the
ACFE (Association of Certificated Fraud Examiners) 2016 report, it has
found that firms lost 5 percent of their annual income due to fraud and the
total loss caused by the fraud was $ 6.3 billion. Turkey is in a similar
situation privately; according to the GIB (Income Administration Authority)
2017 annual report, Turkey taxpayers examined, which constitutes 1,9
percent of total taxpayers, the tax loss determined per taxpayer 126.000 TRY
and base average was declared 10 percent missing in all types of taxes in
2017. According to the estimates, it is emphasized that the ratio of informal
economic activities and tax loss to fluctuating course varies between 86.73
percent and 35.37 percent (Erkus and Hakan, 2009: 139). According to the
OECD Tax Administration 2013 report, the taxation rate of developed
countries is generally over 50 percent. The updated OECD report on 2015
Turkey’s data presented as percent of 25. The labour force in Turkey at the
same time the population is rather low rate of taxation. Most of field studies
shows informal economy size in Turkey has shown as more than percentage
of 40 as a high rate similar with developing countries. Because of this
negative view Turkey companies selected for this research.
There are number of problems that weaken the effectiveness of auditing.
First of all, most of the financial data is kept in the organizations and there
are obstacles to access. Controlling financial data is a time-consuming and
expensive process. Declared public and publicly available financial data is
limited. Various methods have used to gain an idea of the correctness of
financial statements. Those are generally based on the analysis of various
financial ratios. In spite of this, Benford's law, which is based on the
frequency of the figures in the number digits, is another way of finding
practice in fraud control, even though there doesn’t has financial
infrastructure. However, for both methods need generally accepted critical
values to able to make significant comparing.
Most of the frauds in the financial world are based on changing the numbers.
Detecting the changed numbers in this case also means to reveal the fraud.
Benford's law makes it possible that states that the probability of finding
numbers in a naturally occurring number of digits is not equal. Nigrini
(2000) has stated that the Benford’s law became a powerful and valuable
tool for the audit of accounting. Benford's law is characterized by being
observable only in naturally occurring numbers, not artificially regulated
numbers. These numbers are no longer naturally occurring in the case of a
random accounting record, and the Benford’s law can be effective in
determining those (Bhattacharya et al., 2008: 150). This is what makes
Benford's law extremely useful in detecting fraudulent financial data. In the
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case of fraudulent interference with financial accounts, generated figures
will not comply with Benford's law, thus increasing the chances of being
perceived by analysis techniques based on this law. However, the fact that a
data set does not comply with the Benford’s law is not sufficient for fraud
detection alone. This provides only some statistical evidence that the data
may have been manipulated. Therefore, when interpreting the results of the
Benford’s law, it is necessary to consider certain limitations (Bhattacharya et
al., 2010: 577). Auditors should use analytical procedures to determine the
presence of abnormal operations, incidents and trends. The Benford’s law
provides the expected forms of numerical data and has proposed as a test for
the specificity and reliability of accounting data at the transaction level
(Nigrini and Miller, 2009: 305). Hundreds of studies in the literature have
shown that Benford's law can be applied to accounting data, and it could
work well for fraud detection.
This paper contributes to the literature in several ways. First, this study is the
first study, which use “BDS” (Benford Digit Score) and “BDS critic values”
for determining financial statements compliance with Benford’s law and also
separated from other studies in terms of use BDS as a detecting method of
fraud. BDS critical values as conformity table which is a development about
Benford analysis is effective, basic and fast method to detecting fraud.
Secondly determined some nonfinancial factors, which are effective on
financial statements fraud risk. It also suggests usage of Benford analysis as
a comparing method between groups. There are five factors that examined is
there any relations or what is the direction of relation between fraud and
those nonfinancial factors; sectors which companies traded in, risk groups
which authority of stock market classified, audit firm size, independent audit
regulation and independent member quantity in directory board. We found
that as expected there is difference between sectors about financial
statements fraud risk. That has found negative relation between risk groups,
audit firm size, independent audit obligation and independent directory
member quantity and fraud risk. It has expected that it will be a guide to the
researcher about the factors which are effective in the financial statement
fraud. At the same time it is presenting a proof that BDS values works
effectively for measuring conformity with Benford’s law. Company owners,
professional accountants, auditors and tax authority can use this method for
detecting red flags and selecting audit targets.
The structure of the paper is as follows; section one; background and
motivation of study. Section two; review related previous studies. Section
three; outlines the conceptual basis for Benford`s law theory. Fourth section;
explain the research method and rationale behind the study. Section five;
summaries and discusses the results obtained. This is followed by sixth
section, which concludes the study and suggests further research.
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2. LITERATURE REVIEW
The application of Benford's law to accounting data was first made by
Carslaw (1988). In Carslaw's study, the frequency of second digit (especially
zero) appearances in the earnings numbers of New Zealand companies was
investigated in accordance with Benford's law. In particular, reported gains
have a much higher expected 0 frequency and a lower expected 9
frequencies. It has been stated that this abnormality may be evidence of an
alternative income-healing behaviour that firms want to reach. According to
the author, such targets are based on the existence of cognitive reference
points.
Since then Benford analysis has found a wide range of applications in
accounting with a wide variety of methods. The analysis can be applied to
any kind of accounting data that has a number of records over a certain size
and is expected to occur naturally; generally accounting statements, financial
statements, tax declarations and macroeconomic data. Nigrini (1994)
conducted a case study on embezzlement by a security chief working in a
large housing estate. The first two-digit combinations of fraudulent checks
gave a hint to the fraudulent process of deviation from Benford's law. In
Hill's (1998) analysis of a 1995 tax bill that has known to be fraudulent in
New York, it was determined that the numbers did not follow Benford's law.
In Nigrini (1995) study, Benford's analysis of US President Clinton's tax
payments, 13-year tax payments have been observed to follow the Benford
distribution. Tam Cho and Gaines (2012) test Benford's years of US election
campaign spending. In 2011, an analyst of China-based company
implemented the Benford first and second digit tests using the data from the
last 10 quarters of the income statement, and found that 5 and 8 figures were
higher than expected and 4 figures were lower than expected. This situation
has come to the conclusion that the 4 figures of Chinese culture originated
from the fact that the ominous 5 and 8 figures were regarded as auspicious,
and they may have been played with figures (Özdemir, 2014).The summary
information of similar studies is given in the Table 1.
Table 1: Benford’s Law Applications in the field of Accounting
Author
Year
Variables
Digit Test
Conformity Tets
Carslaw
1988
Net Profit, Ordinary
Profit
1.and 2.
Z-statistics
Thomas
1989
Profit-loss, quarterly
profits, earnings per
share
1.and 2.
Z-statistics
Chiristian and
Gupta
1993
Income Tax Returns
First 2
Z-statistics
Nigrini and
Mittermaier
1997
Invoices
1. 2. and First 2.
Z-statistics
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Niskanen and
Keloharju
2000
Net Profit
2.
Z-statistics
Kinnunen and
Koskela
2003
Net profit and loss
2.
Z-statistics
Das and Zhang
2003
Earnings per share
2.
Z-statistics
Skousen all
2004
Profit
1. 2. 3. and 4.
Z-statistics
Durtschi
2004
Purchase checks,
insurance claims
1.
NA
Çakır
2004
Stock closing
1., 2.and First 2.
MAD test
Quick and
Wolz
2005
Financial Statements
First 2
Chi-square, Z-
statistics
Tam Cho all
2007
Election Campaign
financing
1.
Euclidean distance
Akkaş
2007
Stocks accounts
1., 2. and First 2.
Chi-square
Dorfleitner and
Klein
2008
Stock price
2.
Chi-square
Jhonson
2009
Earnings per share
1.
Z-statistics
Jordon all
2009
Sales
2.
Z-statistics
Krakar and
Zgela
2009
Swift messages
1. 2. and First 2
Chi-square
Çubukçu
2009
Payment Checks
First 2
Chi-square
Jordon and
Clark
2011
Profit
2.
Z-statistics
Archambault
2011
Financial Statements
1.
Chi-square
Rauch
2011
Macroeconomic Data
1.
Chi-square
Henselmann
2012
XBRL Filings
1.
Chi-square, Z-
statistics, MAD test
Tilden and
Janes
2012
Financial Statements
1.
Z-statistics
Hsieh Hsieh
and Lin
2013
Quarterly profits
2.
Z-statistics
Jhonson and
Weggenmann
2013
Financial Statements
1.
Z-statistics, MAD
test
Boztepe
2013
Budget revenues and
expenditures
1.
NA
Yanık and
Samancı
2013
General administrative
expenses
1., 2., First 2 and
First 3
Chi-square
Möller
2014
Financial Statements
2.
z-stat, Chi-square
Uzuner
2014
Financial Statements
1.
Chi-square
Geyer and
Drechsler
2014
Long Term Debt
1.
Chi-square,Z-
statistics
Gönen and
2014
Stocks trading volume
1., 2.and First 2.
MAD test
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Rashan
Demir
2014
Marketing sales
distribution expenses
1., 2.and First 2.
Chi-square
Nigrini
2015
Financial Statements
First 2
MAD test
Amiram all
2015
Financial Statements
1.
FSD score
Nigrini
1992,
1996
Income Tax Returns
1. 2. and First 2.
Chi-square, Z-
statistics MAD test
Nigrini
1994,
2000
Payrolls
First 2
Z-statistics; MAD
test
Van Ceneghem
2002,
2016
Profit, Financial
Statements
2.
Chi-square, z-stat,
MAD test
The study also investigates the effects of risk factors in the field. Risk factor
‘red flags’ that related to fraudulent financial reporting may be grouped in
the following three categories (SAS No. 82): (a) Management’s
characteristics and influence over the control environment, (b) Industry
conditions, (c) Operating characteristics and financial stability (Spathis et al.,
2002: 515). In addition, there are many variables that can be associated with
fraud in financial transactions. For example, the growth rate of the company,
the number of independent executives in the management team, the size of
the audit firm, the stock market index, audit procedures, audit laws and
institutions.
There are limited studies on the factors that affect the risk of fraud in
financial statements. Beasley et al. (2000) has found that the types and ratios
of fraud differed between the three different sectors. It has been emphasized
that having an independent audit committee reduces the risk of fraud. Abbott
et al. (2000) have found that the number of members of the board of
directors without similar affiliation is inversely related to the fraud risk.
Brazel et al. (2006) investigated whether publicly available nonfinancial
measures (NFMs), such as the number of retail outlets, warehouse space, or
employee head counts, can be used to assess the likelihood of fraud.
3. THEORY AND HYPOTESES
3.1. Benford’s Law
The emergence of Benford's law is based on a two-page article published in
the American Journal of Mathematics in 1881 on the frequency of numbers
on the number of digits by American astronomer and mathematician Simon
Newcomb's. Newcomb has shown that the frequency of digits (0-9) is not
equal and he shows the possibility of each digit being in different digits of
the number (Newcomb, 1881: 39). Accordingly, the frequency of the first
digit in the first step decreases from 1 to 9. In step 3, the probabilities are
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very close to each other, and from the fourth step onwards the difference
becomes unclear (Newcomb, 1881: 40).
Newcomb's model has been almost forgotten for 57 years despite all the
excitement and functionality until famous physicist Frank Benford's made
similar observations. Benford showed on the table the frequency of each
digit in the number of digits taking the average of the distribution results
obtained from 20,229 different data sets in the 1938, edition of The Law of
Anamorphous Number in the American Philosophical Society (Benford,
1937: 553). From 20 different data sets, on average, 30.6 percent of the total
20,229 data starts with 1. The ratio of those start with 2 is 18.5 percent on
average, and this ratio is decreasing by the number grows. Benford (1938)
formulated these conclusions in a distribution hypothesis to be called the
"Benford Law", a universal law regulating the digits of numbers.
Table 1: Probabilities predicted by Benford’s Law
di
P(d1)
P(d2)
P(d3)
P(d4)
0
0.119
0.101
0.100
1
0.301
0.113
0.101
0.100
2
0.176
0.108
0.100
0.100
3
0.124
0.104
0.100
0.100
4
0.096
0.100
0.100
0.100
5
0.079
0.096
0.099
0.099
6
0.066
0.093
0.099
0.099
7
0.057
0.090
0.099
0.990
8
0.051
0.087
0.098
0.099
9
0.045
0.085
0.098
0.099
(Source: Deikman, 2007: 323)
Table 2 shows calculated probabilities of occurring numbers in digits.
Benford, the first figures in data sets collected from a variety of fields has
shown almost the same distribution. Benford's law is strong enough to raise
suspicions about the authenticity of data sets that do not comply with this
distinction (Benford, 1957: 551). This rule is also regarded as a universal
nature law because it maintains its validity when scale and number base
change (Fewster, 2012: 27).
The Benford’s law, based on the principle that people cannot produce
numbers by chance, is an example of Hill's (1998) experiment. In the course
of the theory of probability, one group was asked to write 200 rounds of the
results of the coin tour, and the other group was asked to write the estimated
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results of the 200 rounds. Although the same face-to-face situation often
occurs six times in succession in practice, this scenario has never been seen
in the prediction group results (Hill, 1998: 362). In another experiment, 742
students were asked to create random 6-digit numbers, and the numbers were
found to be less compatible or incompatible with Benford's law (Nigrini and
Mittermaier, 1997: 56). This test also repeated by us. In accounting final
examination, students were asked for write randomly six-digit numbers on
paper. As a result of the Benford analysis of the data set collected from 343
papers, appears that the data set is incompatible with the Benford’s law. If
people are asked to generate random numbers, their response will indeed
vary significantly from random sequences (Hill, 1988: 967). When people
think they are producing a random number, they often reflect on their own
experiences and the numbers in their experiences.
Benford's law is a general law concerning naturally occurring numbers that
maintain their validity under different circumstances. Pinkham (1961) stated
that if there is a law governing digital distributions, it is a premise that this
law is constant in terms of scale. So, if the lengths of the world’s rivers
follow a kind of law, it should be insignificant that these numbers are
expressed in miles or kilometres. This means that if all the numbers in a data
set appropriate to the Benford’s law are multiplied by a non-zero constant,
the new set will follow the Benford’s law (Nigrini, 2011: 30). If you apply
this law to the monetary system, the consequences of the data being
denominated in Dollar, Euro, Pound, Peso, Yen or Lira does not change, so
there is no need to deal with the exchange rates (Geyer and Williamson,
2004: 232).
3.2. General Formula
The approximate values of the expected frequencies from Benford's
observations can be calculated by the logarithmic formulation. The
probability of having a significant non-zero number in the first digit of the
number calculates as follows (Hill, 1998: 358):
(1)
For example the probability that the first digit of a number is 6:
P(D1=6) =log(1+1/6)=0,0669 = %6,69
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Figure 1: Frequency of The Numbers In The First Digit
(Source: Raimi, 1969: 109)
Figure 1 shows that the expected frequencies calculated by Benford's
observations and the logarithmic formula calculations are very close to each
other. Likewise, the probability of a digit being in the second digit of the
number can be calculated by the following formula:
(2)
P: Probability
D1: First digit
D2: Second Digit
D3: Third Digit
3.4. Benford Analysis
There are five most important tests for the use of the Benford’s law. These
are; the first digit test, the second digit test, the first two digit test, the first
three digit test and the last two digit test. The first and second digit tests are
high level conformity tests in the selection of data. The First Two Digits and
the First Three Digits tests can be used to select audit targets. The Last Two
Digits test is a strong test without detecting the derived digits, it can be used
to determine the rounding (Nigrini, 2011: 150). The poor compatibility of
data sets with Benford’s law may be a signal of an anomaly related to the
data. Therefore, if a researcher with four datasets in hand has one set of
incompatibility while being compatible with three sets of Benford, the
strategy should be to focus on incongruities, because fraud risk will be
higher (Nigrini, 2012: 74). Data sets to be tested for compliance with the
Benford’s law should meet the following requirements (Quick, 2005: 1290).
The dataset should define the size of similar occurrences; the data must
express the same kind events. The example is all city-based or all-year sales.
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The lower or upper limit of the values in the dataset should not exist. The
maximum and minimum limits disrupt the distribution. The values in the
data set should not be assigned numbers. It is one of the main conditions of
the law that the numbers are formed randomly from the natural way (Akbaş,
2007: 196).
Previous studies about fraud show that the risk of fraud between companies
which traded different sectors is not equal. Brazel et al. (2006) recorded
research results has provided empirical evidence suggesting that
nonfinancial factors can be effectively used to assess fraud risk. According
to the ACFE 2016 report, banking and financial services, government and
public administration and manufacturing industries have represented as the
most represented sectors in fraud cases. In the same report less fraudulent
sectors have determined as publication and telecommunication sectors. Kroll
Global Fraud & Risk Report 2016 has shown similar results. This report
recorded that faced with a fraudulent transaction in last sectors are finance:
percent 87, professional services percent 84, retail percent 82 and medical
percent 80. And average rate of financial fraud has been percent 15 in all
fraudulent transactions. The difference between intersectoral financial
transactions, the frequency of cash and recording transactions, the variety of
financial and administrative obligations, can also influence the quality of
financial statements. Because of the above reason, this study proposing the
fallowing hypothesis.
Hypothesis 1. There is significant difference between sector groups of
companies about fraud risk in financial statements. BIST has classified
companies traded in stock market by risk levels, which calculated according
to some indicators. Shares in the A, B and C groups shall be determined by
the general calculations to be made on a monthly basis for each share of the
shares traded in the stock exchange. For the shares to be made in the D
group, the market and the platform should be taken into consideration
(http://www.borsaistanbul.com/urunler-ve-piyasalar/piyasalar/pay-
piyasasi/a-b-c-d-grubu-paylar). We expected the low risk level companies as
remarked A, has less fraud risk. Mock and Turner (2005) founded that over
two years of audits, number and type of fraud risk factors identified differs
across clients, industries, and fraud risk categories. This study is proposing
the following hypothesis.
Hypothesis 2. There is negative relation between risk groups of companies
from A to D and fraud risk in financial statements. We are expecting the big
four audit firms has more pressure and facilities to qualified the audit
process. That is why they have international prestige, brand value, corporate
governance. Karacaer and Ozek (2010) expressed results of their study,
showed a negative and statistically significant relationship between the size
of the audit firm and the profit management. There are two theoretical
explanations of the relationship between the size of the audit firm and the
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quality of audit (Lennox, 1999: 779). One of these explanations is that large
audit firms are more inclined to publish accurate and accurate audit reports
because they are more famous. According to the alternative hypothesis, the
higher the asset levels (wealth) of the auditors, the more likely they are to
publish the right audit reports in order to protect their assets against the
possibility of a lawsuit (Karacaer and Ozbek, 2010: 62). Francis et al.
(1999), for a large sample of NASDAQ firms, determined that the first six
large audit firms restricted their profit management behaviors. Bauwhede,
Willekens and Gaeremynck (2003) emphasized that there is a significant
difference between the first six audit firms and other audit firms in terms of
the quality of audit. Butler, Leone and Willenborg (2004), Chen, Lin and
Zhou (2005), Chia, Lapsley and Lee (2007), Lennox (2008) Lai (2009),
Korpi et al. (2016) in the works, they found that auditing by big audit firms
decreasing financial manipulation. This study is proposing the following
hypothesis.
Hypothesis 3. There is negative relation between audit firms size and fraud
risk in financial statement. The purpose of the independent audit is to
increase the level of confidence that target users have in their financial
statements. This objective has achieved by the auditor's opinion that the
financial statements are prepared in accordance with the financial reporting
framework in all material respects. It is about whether or not the financial
statements are presented in all material aspects of the financial statements in
a fair manner, or if they provide a true and accurate view (Türkiye Denetim
Standarları BDS200,4). In the audit progress, it is a necessity for the audit to
be performed by an independent auditor based on the information risk. These
conditions can be explained for reasons such as the conflict of interest, the
complexity of the accounting system and the fact that the information is
related to the decisions to be taken (Kepekci, 1996: 9). Yıldız and Baskan
(2014) stated that the independent external audit was the third most
important method with 16 percent in the ways used in accounting errors and
tricks and the independent audit for companies gained importance in
determining the errors and tricks in accounting. As in previous researches of
ACFE, independent external audit has been identified as the most widely
used anti-fraudulent method in the ACFE 2016 report. In the report,
approximately 82 percent of the organizations subject to the same survey
were subject to independent audit and similarly, 81.1 percent were registered
in the Code of Conduct. The existence of anti-fraud controls, as well as the
correlation analysis related to lower fraud losses and faster detection,
showed that fraud losses were 14.3 percent – 54 percent lower in
organizations with controls to combat fraud than those without controls, and
33 percent of tricks. 3 – 50 percent has found to be detected more quickly.
This study is proposing the following hypothesis.
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Hypothesis 4. There is positive relation between Independent Audit
Regulation and fraud risk in financial statements. Corporate governance, in
the management and operation of the companies, the protection of the rights
of all stakeholders, including shareholders, as well as their traditional
structures, which take into account the rights of the community in question,
in other words, to obtain and distribute profit to their shareholders, is a
management philosophy that aims to regulate the rules of relations (Sehirli,
1999). As an essential element of the corporate governance approach,
internationally applicable; four key elements identified as fairness,
responsibility, transparency and accountability (Pamukcu, 2011: 135).
Meantime, social responsibility and neutrality principles, which are among
the basic concepts of accounting, also recommends that the resources are
evaluated fairly, regardless of the entity's interest groups (Ozkol et al., 2005:
138). Xie et al. (2003) conclude that board and audit committee activity and
their members financial sophistication may be important factors in
constraining the propensity of managers to engage in earnings management.
Dechow and Dichev (2002), Peasnell et al. (2000) studies based on data for
US and UK firms document that corporations with independent boards tend
to have less financial manipulations. Jaggi et al. (2009) evaluates there is
association between corporate board independence and earnings
management in Hong Kong firms. This study is proposing the following
hypothesis.
Hypothesis 5. There is negative relation between independent directory
board member quantity and fraud risk in financial statements. The effect of
the structure of the board of directors on business performance has recently
attracted the attention of researchers. In the Corporate Governance
Communiqué published by the SPK, it is obligatory to have an independent
member within the board of directors. It is also regulated that the number of
independent members cannot be less than one third of the total number of
members and in each case less than two. The reason for this regulation is
transparency and accountability within the scope of corporate governance
principles, so a positive relationship can be expected between the number of
independent board members and the risk of fraud. Atılgan (2017); Şengür ve
Püskül (2011); Özen ve Yılmaz (2016); Demirel (2014) found relations
between independent member rate and financial ratios and transparency.
4. METHODOLOGY
4.1. Data Set
The research universe is companies traded at BIST. Based on 2017, the
companies traded at BIST have been identified and the same companies
have been included in the research universe for 10 years towards the past. In
this period in Turkey stock market has averagely 400 companies. Banking
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and finance companies excluded from this research in order to have different
form of financial statements. In this framework, the number of companies
that disclosed their financial data to the public is 347. In this way, all of the
research subjects are included. The study attempted to analyse quarterly
balance sheets and income statements covering this 347 companies period
between 2008 to 2017, were used as data set. However, the universe size and
the research period may differ for the five different variables in sections
investigated in the study. In the sections where each variable is investigated,
data set information is given separately. The data is collected from FINNET
database and official web site of Turkey stock market; ww.kap.org.tr.
In order to measure compliance with the Benford’s law, T-Test and ANOVA
tests have applied to investigate the differences according to BIST risk
groups, sectors, before and after independent audit regulation, independent
audit firms and independent directory board membership. The TUKEY test
has used for multiple comparisons. SPSS 22 program has been used for these
tests.
4.2. Research Design
In this section, it was researched whether there is a significant difference in
financial statement fraud risk between different groups of companies
operating in BIST. The intent of the financial statement fraud risk is that the
financial statement data conform to the Benford’s law. For this purpose, the
data set was subjected to the 1st Digit test, 2nd Digit test and First-2 digit test
under the Benford analysis. From the result, BDS (Benford Digit Score)
values calculate for each observation. According to BDS critic values table
(Table 3) companies classified as compatible or incompatible. BDS is
different version of MAD (Mean Absolute Deviation) and calculates by
taking average of digit test MAD values.
MAD is calculates as below (Nigrini, 2001: 158):
(3)
AP: Actual Distribution,
EP: Benford distribution, K: 9 (for first digit test), 90 (for first 2 digit)
BDS calculates as (4)
Table 2: BDS Critical Values Table
BDS Value
Result
0,000 - 0,0095
Comformity
0,0095 - 0,0157
Acceptable Comformity
>0,157
Nonconformity
(Source: Ozevin, 2018: 116)
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Companies with a lower BDS value are more compatible, while those with a
higher BDS value are considered more incompatible. The compliance of the
financial statements of the company with Benford's law is interpreted as the
indication of the low fraud risk of the financial statements.
5. RESULTS AND DISCUSSION
5.1. Sector-Based Analysis of Financial Statements
The financial statements of companies operating in the real sector in BIST
were subjected to Benford analysis according to the sectorial classification.
According to the BIST sector classification, sectors that are at least 10
companies in each sector from the companies operating in the real sector are
included in the research. If there are less than 10 companies from a sector
group, that sector is left out this research. In this way, the entire research
universe consists of 179 companies. This part of the research covers five
years of data between 2013-2017. Calculated BDS values are compared with
critical values and compliance levels are determined for annual and five-year
periods. Sectors included in the analysis consist of companies that can
achieve the full data set. The financial sector and holding companies are
excluded from the analysis in this section.
Table 3: Compormity-Noncomformity Companies According to Benford
Analysis Results
5 Years (2013-2017)
Annual (2017)
Sectors
Company
Compatible
Incompatible
Compatible
Incompatible
Food
25
18
7
19
6
Clothing
19
13
6
12
7
Chemistry
25
19
6
20
5
Trade
18
9
9
10
8
Metal
14
8
6
10
4
Stone-Soil
23
10
13
14
9
Paper-Publication
14
8
6
10
4
Technology
15
11
4
9
6
Machinery
26
18
8
15
11
Total
179
114
65
119
60
According to Table 4, of the 179 companies whose financial statements for
2017 were analysed, 114 companies were in compliance with Benford's law
and 65 were out of compliance. Of the 25 firms in the food sector, 19 were
compatible and 6 were incompatible. The most incompatible companies
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appears in Trade sector and the least incompatible companies seen in the
Chemistry sector. Comparative analysis and significance tests on the
sectorial basis were carried out in the next section.
Table 4: Comparing Sector Conformity with Benford's Law
Sum of Squares
df
Mean square
F
Sig.
Between Groups
,000
8
,000
2,490
,011
Within Groups
,013
1561
,000
Total
,014
1569
Table 5 compares the sectors according to conformity to Benford’s law. The
results of the analysis show that the sectors differed in terms of compliance
with the Benford’s law.
Table 5: Descriptive Statistics by Sectors
N
Mean
Std.
Dev.
Minimum
Maximum
Multiple
Comparsion
Food
230
,0151
,00292
,01
,02
Clothing
180
,0156
,00317
,01
,03
Metal Goods
Paper-Pabl.
140
,0150
,00269
,01
,02
Chemistry
200
,0150
,00312
,01
,03
Trade
Stone-Soil
230
,0150
,00290
,01
,03
Trade
Metal Main
120
,0151
,00266
,01
,02
Metal
Goods
230
,0148
,00282
,01
,02
Chemistry,
Clothing
Trade
100
,0161
,00294
,01
,02
Chemistry, Metal
Goods
Technology
140
,0151
,00309
,01
,03
Total
1570
,0152
,00295
,01
,03
Multi-sectorial comparison was conducted by the TUKEY test. When the
analysis results are examined, it is seen that there is a statistically significant
difference between the clothing sector and the metal goods sector, between
the trade sector and the chemical and metal goods sectors, between the
chemical and trade sector, between the stone-soil sector and the trade
sectors. According to the BDS values, it is seen that the sector which is most
compatible with the Benford’s law is the metal goods sector with 0.148. The
most incompatible sector is the trade sector with an average MAD of 0.0161
(Table 6).
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5.2. Risk Groups Based Analysis of Financial Statements
BIST includes all companies traded on the risk factor classification. Risk
classification is done annually. Therefore, this part of the research covers
only the data of 2017. The number of companies traded at BIST in 2017 is
413. Since the companies that have not disclosed financial data are also
listed in this classification by BIST, the research universe has increased to
413. The entire research universe is included in the research. BIST separate
companies for four groups according to their risks factors. As go from A to
D, the companies with increasing risk scores are listed with specific
calculations. In the second half of 2017, there were 253 companies listed in
group A, 76 companies in group B, 35 companies in group C and 49
companies in group D. The Anova test results for the risk groups are shown
in Table 7.
Table 6: Anova Test Results by Risk Group
Sum of
Squares
df
Mean
square
F
Sig.
Between Groups
,001
3
,000
20,585
,000
Within Groups
,010
1100
,000
Total
,010
1103
It can be said that there is a significant difference between the BIST risk
groups in terms of compliance with the Benford’s law and hence the
accuracy of the financial statement because the p-value of the analysis
according to Table 7 which shows the results of Anova test is less than 0.05.
Table 7: Descriptive Statistics by Risk Groups
N
Mean
Std. Dev.
Minimum
Maximum
A
828
,0147
,00288
,01
,03
B
171
,0162
,00350
,01
,03
C
66
,0167
,00294
,01
,02
D
39
,0156
,00297
,01
,02
Total
1104
,0151
,00307
,01
,03
As seen in Table 8, the BDS value of the companies classified as low risk by
the BIST were low and the companies classified as high risk were high. It
can be said that the BDS score gives parallel results to the BIST
classification when making risk estimation.
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Table 8: Multiple Comparison Results by Risk Groups
(I) Risk Group
(J) Risk
Group
Mean Dif. (I-J)
Std. Error
Sig.
A
B
-,00158*
,00025
,000
C
-,00202*
,00038
,000
D
-,00092
,00049
,239
B
A
,00158*
,00025
,000
C
-,00045
,00043
,730
D
,00066
,00053
,606
C
A
,00202*
,00038
,000
B
,00045
,00043
,730
D
,00110
,00060
,262
D
A
,00092
,00049
,239
B
-,00066
,00053
,606
C
-,00110
,00060
,262
Table 9 shows the results of the TUKEY test, which shows the differences
between the risk groups. Accordingly, there is a significant difference
between the companies included in the risk group A and the companies
included in the groups B and C in terms of financial statements fraud risk.
This is a finding that confirms that the companies in Group A in the BIST
risk classification differ from those in the other group in terms of financial
health.
5.3. Audit Firm Based Analysis of Financial Statements
The purpose here is to investigate whether there is a significant difference in
the fraud risk of the financial statements between the companies audited by
the Big Four audit firms (Deloitte, EY, KPMG, PwC) and the other audit
firms. In order to measure the effect of the audit firm on the financial
quality, BIST companies were subject to Benford analysis by separating two
groups according to independent audit firms. The first group consists of
companies audited by Deloitte, Ernest Young, KPMG and PwC firms known
as the Big Four audit firms in the world, and other auditing firms audit the
second group. According to the independent audit reports of 2017 and 2016,
there are 194 companies that have signed an audit agreement with Big Four
audit companies over a two-year period, and 152 companies that have signed
agreements with other audit companies.
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Table 9: BDS Values of Companies According to Audit Firm
Audit Firm
N
Mean
Std.
Deviation
T-Test Sig.
Other
326
,0165465
,00453198
0,024
Big Four
389
,0156045
,00626821
As seen in Table 10, the companies audited by the Big Four audit firms have
a better score in terms of BDS values indicating the fraud risk of the
financial statements. This situation can be interpreted as being branded and
international supervisory firms performing more effective audit.
Table 10: BDS Compliance T-Test Results According to Audit Firm
F
Sig.
t
df
Sig. (2-
tailed)
Mean Dif.
Equal Variances
assumed
,019
,891
2,263
713
,024
,00094
Equal Variances
not Assumed
2,326
698,534
,020
,00094
When the financial statements fraud risk was measured according to the size
of the audit firm in Table 11, significant differences were found between the
companies audited by the big four audit firm and audited by other audit
firms.
5.4. Independent Audit Regulation based Analysis of Financial
Statements
It has been researched whether there is a significant difference in the
accuracy of financial statements before and after the requirement of
Independent Audit of companies traded in BIST. Independent audit of the
financial statements has been introduced in Turkey since 2013. In this
section, the research period is presented as five-year periods before and after
the independent audit obligation. In this frame, it was researched whether the
financial statements of companies traded in BIST differ from the conformity
to Benford’s law before and after independent audit. In the pre-audit period
taken 2008-2012 years, there are 238 companies that can reach all of the
data, whereas the total number of companies that can be reached during the
independent audit 2013-2017 period is 347.
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Table 11: BDS Values of Before and After Independent Audit Companies
Term
N
BDS
T-Test Sig.
Before Independent Audit (2008-2012)
1,336
0,0111
0,039
After Independent Audit (2013-2017)
1,694
0,0095
Table 12 compares the BDS values of the companies before and after the
independent audit period with the T-test. When the results are examined, it
can be seen that the BDS values before and after the independent audit
period differ in a statistically significant.
Table- 12: The Effect of Independent Audit on BDS Cohesion T-Test
Results
F
Sig.
t
Df
Sig. (2-
tailed)
Mean
Dif.
Equal Variances
assumed
4,605
,032
2,063
1568
,039
,00033
Equal Variances
not Assumed
2,063
1525,286
,039
,00033
According to Table 13, the pre-audit period seems to be weaker than
Benford's law according to the independent audit period. This can be
interpreted as an independent audit having a positive effect on the fraud risk
of the financial statements.
5.5. Independent Directory Member Based Analysis of Financial
Statements
In this part examined independent directory board membership effects on
financial statements accuracy according to Benford’s law BDS values. In
this section, the research universe is 347 BIST companies whose financial
data are available in 2017. BIST companies separated into two groups
according to independent directory board membership rate. The median rate
of 347 companies was 0,33. According to this company groups separated as
which has more than percent 30 independent member rate and which has less
than 30 percent independent member rate.
Table- 13: BDS Values of Directory Board Membership
Group
N
BDS
T-Test Sig.
Independent Member
>percent30
154
.0153007
0.015
Independent Member
<percent30
193
.0157601
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As seen in Table 14, BDS values of more independent member owner
companies is lower comparing to less independent member owner
companies. According to test result observed that the independent directory
board membership have positive effect on the financial statements fraud risk.
6. CONCLUSION
The results of the study can be summarized as follows: As a result of the
application of Benford analysis on a sectorial basis, it has been determined
that the companies operating in different sectors exhibit significant
differences in the level of compliance with Benford's law and therefore the
financial statements fraud risk. Among the nine sectors included in the
study, has shown the most close conformity results were in the chemical
sector and most nonconformity results has occurred in the trade sector. This
can be interpreted as the fact that the risk of fraud is lower in the financial
statements of the companies operating in the chemical sector and the risk is
higher in the companies in the trade sector.
BIST classified the companies according to risk level. Towards low-risk to
high-risk as A, B, C, and D groups. It has founded that there was a
significant difference between these groups in terms of compliance with the
Benford’s law in the study. Group A is significantly different from Groups B
and C in a positive manner. In other words, companies in the lower risk
group are more likely to comply with the Benford’s law.
Another result of the study is that found positive correlation between the size
of the audit firm and the fraud risk of the financial statements. As a result of
the Benford analysis, the financial statements of the companies audited by
the Big four audit firms were more consistent than the companies audited by
other audit firms.
The financial statements of the periods before and after the independent
audit obligation have been determined to be the result of the Benford
analysis and the independent audit observer has increased the fraud risk of
the financial statements. Compared to the 2008-2012 period, the financial
statements for the period 2013-2017 are more compatible with Benford's
law.
When examined the correlation between independent directory member
quantity and fraud risk of the financial statements, it found that companies
which have more independent directory members have closer conformity
with Benford’s law. It can be said that independent directory membership
quantity affected positively the financial statements accuracy.
This study determined some nonfinancial factors, which are effective on
financial statements fraud risk. It is expected that it will be a guide to the
researcher about determining fraud behaviors and related nonfinancial
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factors. It is a finding to be taken into account by auditors, company owners
and tax authorities that the risk of fraud in firms of different sectors is
different. As a result of the fact that large audit firms reduce the risk of fraud
compared to others, the auditor can guide the company managers in selecting
the company. According to the results of the analysis, the positive impact of
the independent audit practice on the fraud risk of the financial statements
may shed light on the new regulations of the relevant authorities. The
number of independent management club members is inversely related to the
risk of fraud. This information can be valuable for managers and investors.
We use BDS values and BDS critic table for measuring conformity with
Benford’s law and by the same way fraud risk of financial statements. It’s a
development about Benford analysis which is effective, basic and fast
method to detecting fraud. Company owners, professional accountants,
auditors and tax authority can use this method for detecting red flags and
selecting audit targets. At the same time it is presenting a proof that BDS
values works effectively for measuring conformity with Benford’s law.
In future research analyses can be repeated for different groups. For
example, it is thought that this analysis can give interesting results for
public-private companies, profit - non-profit companies. Also the
comparison of the companies of different countries with this analytical
method can give useful results.
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