JURNAL RISET AKUNTANSI TERPADU

Vol.14 No.2, 2021

Hal. 194-209

Fraud Diamond Analysis In Fraudulent Financial Statement Detection Using Beneish M-Score

Meri Kristianti
Department of Accounting, Institute of Business and Informatics Kwik Kian Gie
34170037@student.kwikkiangie.ac.id

Carmel Meiden
Department of Accounting, Institute of Business and Informatics Kwik Kian Gie
carmel.meiden@kwikkiangie.ac.id

Abstract

Companies, especially go public companies, tend to want to display financial statements that
look healthy and profitable to attract the attention of investors and potential investors.
Therefore, sometimes companies commit fraud in financial statements to fulfill these objectives.
This study was conducted to empirically test whether the fraud diamond element has an effect
on the possibility of fraudulent financial statements in manufacturing sector companies listed on
the Indonesia Stock Exchange in 2017-2019 with the Beneish M-Score as a proxy for the
dependent variable. There are 120 total samples obtained by purposive sampling method with
analytical techniques using descriptive statistical tests and logistic regression tests. The results
of this study indicate that the nature of industry and rationalization variables have a positive and
significant effect on the possibility of fraudulent financial statements, but for the variables of
financial stability, external pressure, personal financial need, financial target, ineffective
monitoring and capability, it is not proven to have a significant effect on the possibility of
fraudulent financial statements.

Keywords: Fraudulent Financial Statement, Fraud Diamond, Beneish M-Score.

INTRODUCTION

(Ikatan Akuntan Indonesia, 2015) defines financial statements as a structured

presentation of the financial position and financial performance of an entity. The purpose of
making financial statements in general is to provide information for users of financial statements
for making economic decision. Financial statements are very important documents, especially
for go-public companies. Financial statements become one of the main guidelines of
stakeholders in decision making, so that clean, trustworthy and reliable financial statements are
very important.
According to (Indonesian Institute of Certified Public Accountants, 2014) fraudulent
financial reporting includes intentional misstatement including the omission of an amount or
disclosure in the financial statements to influence the perception of users of financial
statements. Companies, tend to want to display financial reports that show positive work results
so that they look healthy and attractive to stakeholders and potential investors. Therefore, fraud
in financial statements that is generally carried out is overstatement of profit.
Financial statement fraud is a type of fraud that has the most detrimental impact among
other types of fraud (Yesiariani & Rahayu, 2017). Cases of financial statement fraud that
occurred abroad, such as the case of Steinhoff International Holdings N.V from South Africa, are
Meri Kristianti, Carmel Meiden 195

indicated to have manipulated profits and assets in 2019. There are also cases of fraudulent
financial statements in the country such as the case of PT. Hanson International Tbk, who is the
president director and independent director committed fraud in the 2016. Furthermore, the
fraudulent financial statement case of PT. Garuda Indonesia Tbk in 2018.
The detection of factors that influence fraudulent financial statements in this study uses
the analysis of the fraud diamond theory. (Wolfe & Hermanson, 2004) believes that the concept
of fraud triangle can be developed for the prevention and detection of fraudulent behavior by
adding capability elements. In this study, the Beneish M-Score was used as a proxy for fraudulent
financial statements. The object of this study is a company listed in the manufacturing sector on
the Indonesia Stock Exchange for the 2017-2019 period.
The purpose of the study is to determine whether financial stability, external pressure,
personal financial need, financial target, nature of industry, ineffective monitoring,
rationalization have a positive effect on the possibility of fraudulent financial statements and
whether capability have an effect on the possibility of fraudulent financial statements. In
addition, this research is expected to be useful for readers to increase knowledge. For investors
or potential investors, it is expected to help in making decision. And for further research, it is
expected to be a contribution of knowledge with similar topics.

THEORITICAL FRAMEWORK AND HYPOTHESIS

Literature Review
Agency Theory
According to (Jensen & Meckling 1976) agency relations are contracts in which one or more
persons (principal) command another person (agent) to perform a service on behalf of the
principal and authorize the agent to make the best decision for the principal. The contract
relationship can have different interests. According to (Tessa & Harto, 2016) the existence of a
conflict of interest between the agent and the principal is often referred to as a conflict of
interest. Principal want to get large profits or high returns from investments, but agent also has
a desire to get greater compensation for their performance results which is this desire can
encourage to commit fraudulent financial statements.

Fraudulent Financial Statement

(Arens et al, 2015: 396) describe fraudulent financial statements as misstatements or
intentional disclosure of amounts or disclosures with the aim of deceiving the users of that
report. According to the Australian Auditing Standards (AAS) (in Norbarani & Rahardjo, 2012)
fraudulent financial statements are a deliberate omission or disclosure of a certain amount or
disclosure in financial reporting to deceive users of financial statements.

Fraud Triangle Theory...

Fraud triangle is a fraud theory proposed by Cressey (in Skousen et al., 2009) which states
to some extent there are three conditions that are always present at the time of financial
statement fraud occurs. SAS 99. AU 316 (in Arens et al, 2015: 398) describes that there are three
conditions of fraud from fraudulent financial statements and asset misappropriation, including
the following:
a. Pressure/Incentive
196 Meri Kristianti, Carmel Meiden

It is a situation of pressure / incentives that encourage managers or other employees

to commit fraud. Pressure conditions that cause fraud consist of financial stability,
external pressure, financial target and personal financial need.
b. Opportunity
It is a situation that opens up opportunities for management or employees to
commit fraud. Opportunity conditions that cause fraud consist of ineffective monitoring,
nature of industry and organizational structure.
c. Rationalization
It is an attitude or set of ethical values that justify or permit fraud, or they are in an
environment that influences to rationalize dishonest acts.
Fraud Diamond Theory
Fraud diamond theory is a theory developed by (Wolfe & Hermanson, 2004) who believes
that the concept of fraud triangle can be developed for the prevention and detection of
fraudulent behavior by adding one element, namely capability. According to (Zaki, 2017)
revealed that the factors of the diamond fraud model are a good tool for assessing the possibility
of financial statement fraud. (Wolfe & Hermanson, 2004) explains that fraud or fraud cannot
happen without a person who has the right ability to carry out such fraud or fraud.

Beneish M-Score Ratio

Beneish M-Score Ratio is a collection of financial ratios used to detect the possibility of
fraudulent financial statements. (Beneish, 1999) conducted research to detect profit
manipulation in the company and find out what drives fraud and divide the company into
categories that are indicated to be fraudulent financial statements and those that do not.
However, detection with Beneish M-Score has the limitation of only being able to detect
cheating in the form of overstatement in go-public companies.

Beneish Ratio Index

According to (Christy & Stephanus, 2018) The Beneish Ratio Index is a technique used to
analyze financial statements in detecting the absence or absence of financial statement fraud.
The Beneish Ratio Index is measured using five ratios: Days Sales in Receivable Index, Gross
Margin Index, Asset Quality Index, Sales Growth Index and Total Accrual to Total Asset Index
ratio.
Theoretical Framework and Hypothesis
1. The Effect of Financial Stability on The Possibility of Fraudulent Financial Statements
SAS No. 99 (in Skousen et al., 2009) explains that managers face pressure to cheat on
financial statements when financial stability or profitability is threatened by economic
conditions, industries and the conditions of operating entities. Financial stability is projected by
the percentage ratio of total changes in assets (Skousen et al., 2009). The greater the value of
the ratio of changes in total assets, the possibility of fraudulent financial statements will also be
higher. Based on the results of research (Sihombing & Rahardjo, 2014) showed that financial
stability has a significant positive effect on fraudulent financial statements. Therefore, the
proposed hypothesis is:
Ha1: Financial stability has a positive effect on the possibility of fraudulent financial statements.
2. The Effect of External Pressure on The Possibility of Fraudulent Financial Statement
When management is under great pressure because of the difficulty to meet the demands
of external parties to meet expectations and requirements (debt), it is possible that it becomes
Meri Kristianti, Carmel Meiden 197

the cause of fraud in financial statements. The greater the value of the company's leverage ratio,
the greater the possibility of fraudulent financial statements. Based on the results of the study
(Sihombing & Rahardjo, 2014) using a leverage ratio that divides total debt by total assets as
proxies on external pressure variables shows that external pressure has a significant positive
effect on fraudulent financial statements. Therefore, the proposed hypothesis is:
Ha2 : External pressure has a positive effect on the possibility of fraudulent financial statements.
3. The Effect of Personal Financial Need on The Possibility of Fraudulent Financial Statement
The existence of share ownership by company insiders causes the concerned to feel they
have a right to claim the company's income and assets so that it will affect the company's
financial condition (Yesiariani & Rahayu, 2017). The higher the value of the ratio, the higher
possibility of fraudulent financial statements. Based on the research by (Skousen et al., 2009)
using the ownership held by company insiders ratio as a variable proxy shows that personal
financial need has a significant positive effect on fraudulent financial statements. Therefore, the
proposed hypothesis is:
Ha3 : Personal Financial Need has a positive effect on the possibility of fraudulent financial
statements.
4. The Effect of Financial Target on The Possibility of Fraudulent Financial Statements
Fraud committed by management is motivated by a conflict of interest described in the
agency theory where in order to achieve the financial targets given by the principal, then
management will do anything including data manipulation on financial statements so that
management can receive rewards/ incentives and avoid pressure from the principal. Return on
Asset is a way to measure management's performance in showing how efficiently an asset has
been used (Skousen et al., 2009). The larger the targeted ROA, the more likely the fraudulent
financial statement is. Based on the results of research (Sunardi & Amin, 2018) showed that
financial targets have a significant positive effect on fraudulent financial statements. Therefore,
the proposed hypothesis is:
Ha4 : Financial target has a positive effect on the possibility of fraudulent financial statements.
5. The Effect of Nature of Industry on The Possibility of Fraudulent Financial Statement
According to Summers & Sweeney (in Sihombing & Rahardjo, 2014) states that accounts
receivables and inventory require subjective judgment in estimating the uncollected receivables
and obsolete inventory because management can use the account as a tool for manipulation of
financial statements. The larger the accounts receivable, the more likely fraudulent financial
statements are. Based on the results of the study (Sihombing & Rahardjo, 2014) using the
changes in receivables ratio as a proxy on the nature of industry variable shows that the nature
of industry has a significant positive effect on fraudulent financial statements. Therefore, the
proposed hypothesis is:
Ha5 : Nature of industry has a positive effect on the possibility of fraudulent financial statements.
6. The Effect of Inffective Monitoring on The Possibility of Fraudulent Financial Statements
An independent board of commissioners is believed to increase the effectiveness of
corporate supervision (Sihombing & Rahardjo, 2014. The smaller the ratio of independent
commissioners to the total board of commissioners means the less effective supervision in the
company, making it more likely fraudulent financial statements. Based on research (Damayanti
& Suryani, 2019) shows that independent board of commissioners increase effectiveness in
supervising management to prevent fraudulent financial statements. Therefore, the proposed
hypothesis is:
Ha6 : Ineffective monitoring has a positive effect on the possibility of fraudulent financial
statements.
7. The Effect of Rationalization on The Possibility of Fraudulent Financial Statements
198 Meri Kristianti, Carmel Meiden

According to (Agusputri & Sofie, 2019) subjective judgment and decision making will be
reflected in the company's accrual value. The principle of accrual can be utilized by management
to perform manipulations that influenced the rationalization of management in decision making.
According to (Beneish, 1999) the higher the TATA ratio means the more likely profit manipulation
is with an increase in accrual transactions in revenue recognition. Based on the results of the
study (Sihombing & Rahardjo, 2014) indicates that rationalization has a significant positive effect
on fraudulent financial statements. Therefore, the proposed hypothesis is:
Ha7 : Rationalization has a positive effect on the possibility of fraudulent financial statements.
8. The Effect of Capability on The Possibility of Fraudulent Financial Statements
(Wolfe & Hermanson, 2004) states that fraud or fraud cannot occur without a person who
has the right ability to carry out such fraud or fraud. The more frequent changes of directors, the
higher the likelihood of fraud. Changes to the board of directors in this study are changes of
dismissed of the board that do not include changes due to expiration or death. Based on research
(Suryani, 2019) states that capability has a positive and significant effect on fraudulent financial
statements. Therefore, the proposed hypothesis is:
Ha8 : Capability has an effect on the possibility of fraudulent financial statements

Figure 1. Theoretical Framework

RESEARCH METHODS

Population & Research Sample

The population in this study was taken from companies going public manufacturing sector
listed on the Indonesia Stock Exchange in 2017-2019. The method used to determine the sample
to be taken for study uses the purposive sampling method. This method is a technique of
determining samples with certain considerations (Sekaran & Bougie, 2017: 67). The following
are the considerations in the determination of samples for this study:
1. Listed manufacturing companies that report financial statements for 2017-2019.
2. Company delisting research period 2017-2019
3. The currency of the financial statements is not in rupiah
Meri Kristianti, Carmel Meiden 199

4. Companies with negative earnings during the period 2017-2019.

5. Data on financial statements related to variables in the study is incomplete.
Dependent Variables
The dependent variable in this study is financial statement fraud proxied by Beneish M-
Score. The formula of the Beneish M-Score:
M-Score = -4.84 + 0.920*DSRI + 0.528*GMI + 0.404*AQI + 0.892*SGI+ 0.115*DEPI – 0.172*SGAI
– 0.327*LVGI + 4,697*TATA
Dummy:
0 = Beneish M-Score value < -2.22, the company is not indicated to have made fraudulent
financial statements.
1 = Beneish M-Score value > -2.22, the company is indicated to have made fraudulent
financial statements.
The details of the Beneish M-Score financial ratio set are as follows:
1. Days Sales in Receivables Index (DSRI)
It is a ratio of comparison between receivables to sales. a large increase in the day of
unnatural or disproportionate receivables to sales may indicate a surge in revenue (Beneish,
1999). The increase in DSRI can have a link to the possibility of recording overstatement sales or
revenues.
𝑁𝑒𝑡 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒𝑠𝑡 /𝑆𝑎𝑙𝑒𝑠𝑡
𝐷𝑆𝑅𝐼:
𝑁𝑒𝑡 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒𝑠𝑡−1 /𝑆𝑎𝑙𝑒𝑠𝑡−1
2. Gross Margin Index (GMI)...
It is a ratio that can assess the level of profitability of a company. If the value of the GMI
ratio > 1 then it indicates that gross profit has deteriorated. Deteriorating gross profit negatively
impacts the company's outlook (Beneish, 1999).
(𝑆𝑎𝑙𝑒𝑠𝑡−1 − 𝐶𝑂𝐺𝑆𝑡−1 )/𝑆𝑎𝑙𝑒𝑠𝑡−1
𝐺𝑀𝐼:
(𝑆𝑎𝑙𝑒𝑠𝑡 − 𝐶𝑂𝐺𝑆𝑡 )/𝑆𝑎𝑙𝑒𝑠𝑡
3. Asset Quality Index (AQI)...
Is the ratio of the ratio between current assets plus fixed assets to total assets. (Beneish,
1999) states that if the value of the AQI ratio > 1, then this indicates that the company has the
potential to increase its involvement in the deferral of costs by raising the value of assets and
lowering liabilities.
1 − (𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝑁𝑒𝑡 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠𝑡 )/𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡
𝐴𝑄𝐼:
1 − (𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1 + 𝑁𝑒𝑡 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠)𝑡−1 /𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1
4. Sales Growth Index (SGI)...
It is a ratio of sales in year (t) to sales of the previous year for measuring sales growth
from year to year. The greater SGI ratio, the higher the possibility of revenue manipulation.
𝑆𝑎𝑙𝑒𝑠𝑡
𝑆𝐺𝐼:
𝑆𝑎𝑙𝑒𝑠𝑡−1
5. Depreciation Index (DEPI)...
It is the ratio of depreciation expense to fixed assets before depreciation. (Beneish, 1999)
states if the value of the DEPI ratio > 1, then this indicates a slowdown in depreciation rate,
which increases the possibility of the company changing the estimated useful life of the asset or
adopting a new method that increases profits.
𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑡−1 /(𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑡−1 + 𝑃𝑃𝐸𝑡−1 )
𝐷𝐸𝑃𝐼:
𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑡 /(𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑡 + 𝑃𝑃𝐸𝑡 )
6. Sales General and Administrative Expenses Index (SGAI)......
200 Meri Kristianti, Carmel Meiden

It is a ratio of comparisons between total sales expenses, general & administration to

sales. According to (Beneish, 1999) a disproportionate increase in sales with sales, general and
administrative expenses is a negative signal about the company's future prospects.
𝑆𝐺𝐴 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑡 /𝑆𝑎𝑙𝑒𝑠𝑡
𝑆𝐺𝐴𝐼:
𝑆𝐺𝐴 𝐸𝑥𝑝𝑒𝑛𝑠𝑒𝑡−1 /𝑆𝑎𝑙𝑒𝑠𝑡−1
7. Leverage Index (LVGI)...
It is a ratio of the amount of debt to the total assets. According to (Beneish, 1999) the
value of the LVGI ratio > 1 indicates an increase in leverage which the higher the LVGI ratio
indicates that the more likely the company manipulates profits to meet its obligations.
(𝐿𝑜𝑛𝑔 𝑡𝑒𝑟𝑚 𝐷𝑒𝑏𝑡𝑡 + 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑡 )/𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡
𝐿𝑉𝐺𝐼:
(𝐿𝑜𝑛𝑔 𝑡𝑒𝑟𝑚 𝐷𝑒𝑏𝑡𝑡−1 + 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠𝑡−1 )/𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1
8. Total Accruals to Total Assets (TATA)
Is the ratio of the total value of the company's accrual to the total asset. The higher
(positive) the value of TATA ratio indicates the more likely the company is indicated to be
manipulating profits through an increase in accrual transactions in revenue recognition (Beneish,
1999).
𝐼𝑛𝑐𝑜𝑚𝑒 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑡 − 𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑡
𝑇𝐴𝑇𝐴:
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡
Independent Variables...
a. Pressure:
1. Financial Stability
Financial stability is a situation that shows the financial condition of a company in a
normal and fine situation (Lestari & Nuratama, 2020). Assets growth (ACHANGE) can be used to
look at a company's financial condition because assets with the formula:
(𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡 − 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1 )
𝐴𝐶𝐻𝐴𝑁𝐺𝐸: 𝑥 100%
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1
2. External Pressure
External pressure is excessive pressure on management to meet debt and third-party
expectations. (Skousen et al., 2009) explain that to cope with the pressure, additional debt or
external sources of financing are needed to remain competitive. This study uses leverage ratio
(LEV) as a proxy for external pressure with the formula:
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑏𝑡
𝐿𝐸𝑉:
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠

3. Personal Financial Need

Personal financial need is a condition when a company's finances are affected by the
personal financial circumstances of a person within the company (Skousen et al., 2009). The
study used the ownership in the firm hold by insider (OSHIP) ratio with the formula:
𝑂𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝 𝑖𝑛 𝑡ℎ𝑒 𝑓𝑖𝑟𝑚 ℎ𝑒𝑙𝑑 𝑏𝑦 𝑖𝑛𝑠𝑖𝑑𝑒𝑟
𝑂𝑆𝐻𝐼𝑃:
𝐶𝑜𝑚𝑚𝑜𝑛 𝑠ℎ𝑎𝑟𝑒𝑠 𝑜𝑢𝑠𝑡𝑎𝑛𝑑𝑖𝑛𝑔
4. Financial Target
Financial target is a target that must be achieved by management. Excessive financial
targets on management can be a boost to profit manipulation. Return on Total Assets (ROA) is a
measure of operating performance used to show how much efficiency an asset has used with
the formula:
𝐼𝑛𝑐𝑜𝑚𝑒 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥
𝑅𝑂𝐴:
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
b. Opportunity
Meri Kristianti, Carmel Meiden 201

1. Nature of Industry
The nature of an industry or the operation of an entity may provide an opportunity to
engage in the fraud of financial statements. According to Summers & Sweeney (in Skousen et
al., 2009) management uses accounts receivables to manipulate financial statements. The study
used the ratio of receivables to sales (REC) as a proxy of the nature of the industry with the
formula:
𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒𝑡 𝑅𝑒𝑐𝑒𝑖𝑣𝑎𝑏𝑙𝑒𝑡−1
𝑅𝐸𝐶: −
𝑆𝑎𝑙𝑒𝑠𝑡 𝑆𝑎𝑙𝑒𝑠𝑡−1
2. Ineffective Monitoring
Ineffective monitoring or ineffective monitoring is the condition of an internal control
system in a company that does not run properly. The more independent commissioners, it is
expected that the more the company's performance (Sihombing & Rahardjo, 2014). This study
uses ratio of independent commissioners with the formula:
𝑇𝑜𝑡𝑎𝑙 𝐾𝑜𝑚𝑖𝑠𝑎𝑟𝑖𝑠 𝐼𝑛𝑑𝑒𝑝𝑒𝑛𝑑𝑒𝑛
𝐵𝐷𝑂𝑈𝑇:
𝑇𝑜𝑡𝑎𝑙 𝐷𝑒𝑤𝑎𝑛 𝐾𝑜𝑚𝑖𝑠𝑎𝑟𝑖𝑠
c. Rationalization
(Suryandari & Endiana, 2019: 32) explains that rationalization is the act of seeking
justification by people who feel themselves trapped in a bad state. Rationalization can be
measured by total accrual ratio where this ratio can reflect the extent of discretionary
accounting decisions management makes as desired. This study uses Total accrual to Total ratio
with the formula:
𝐼𝑛𝑐𝑜𝑚𝑒 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑡 − 𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔𝑡
𝑇𝐴𝑇𝐴:
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠𝑡

d. Capability
(Wolfe & Hermanson, 2004) stated that the position of CEO, directors, and other division
heads is the determining factor of fraud by relying on these positions that are able to influence
others and with their ability to take advantage of the circumstances in cheating. This study uses
a change of directors as proxy of capability. The change of directors that does not include
changes due to the expiration of tenure or death using dummy variables where given code 1 for
companies that have a change of board of directors during the research period and code 0 for
the opposite.
Methods of Analysis
This research uses logistic regression analysis with the following regression model
equations:
𝐹𝑟𝑎𝑢𝑑
L𝑛 1−𝐹𝑟𝑎𝑢𝑑 = 𝛽0 + 𝛽1 𝐴𝐶𝐻𝐴𝑁𝐺𝐸 + 𝛽2 𝐿𝐸𝑉 + 𝛽3 𝑂𝑆𝐻𝐼𝑃 + 𝛽4 𝑅𝑂𝐴 + 𝛽5 𝑅𝐸𝐶 + 𝛽6 𝐵𝐷𝑂𝑈𝑇 +
𝛽7 𝑇𝐴𝑇𝐴 + 𝛽8 𝐷𝐶𝐻𝐴𝑁𝐺𝐸 + 𝜀

Information:
Fraud : Dummy variable ; 1 = indicated fraud; 0 = not indicated fraud
ACHANGE : Percentage ratio of total asset changes
LEV : The ratio of the amount of debt to assets
OSHIP : The ratio of the level of share ownership of people in the company
ROA : Ratio of net income after tax to total assets
REC : Ratio of receivables to total sales
BDOUT : Ratio of total independent commissioners to total board of
commissioners
202 Meri Kristianti, Carmel Meiden

TATA : Ratio of total accrual to total assets

DCHANGE : Dummy variable change of directors; 1 = there is a change; 0 = the
opposite
𝛽0 : Constant
𝛽1− 𝛽8 : Independent variable coefficient
𝜀 : Cofounding variables

Data Analysis Techniques

The data analysis techniques in this study use descriptive statistical analysis and logistic
regression analysis consisting of the overall conformity test of the, regression model feasibility,
determination coefficient, classification matrix test and partial regression coefficient test.

RESULTS AND DISCUSSION

Descriptive Analysis
According to (Ghozali, 2018: 19) descriptive statistics are analyses that provide an
overview or description of a data viewed from the mean value, standard deviation, variance,
maximum, minimum, sum, range, kurtosis, and skewness of distribution.

Table 1. Descriptive Statistics

N Minimum Maximum Mean Std. Deviation
ACHANGE 120 -.17083 .62034 .1357953 .15945107
LEV 120 .09248 .73250 .3769731 .15803516
OSHIP 120 .00000 .89444 .1447527 .22916291
ROA 120 .00053 .26150 .0736110 .05550494
REC 120 -.09614 .21931 .0062443 .03833106
BDOUT 120 .20000 .60000 .4084806 .09001462
TATA 120 -.13128 .32609 .0360176 .07363789
Valid N (listwise) 120
Source: Data results, 2021

Based on table 1, the results of a descriptive analysis shows that the minimum value of
financial stability is -0.17083, maximum value is 0.62034, mean value is 0.1357953 and the
standard deviation is 0.15945107. The minimum value of external pressure is 0.09248, maximum
value is 0.73250, mean value is 0.3769731 and the standard deviation is 0.15803516. The
minimum value of personal financial need is 0.00000, maximum value is 0.89444, mean value is
0.1447527 and a standard deviation is 0.22916291. The minimum value of financial target is
0.00053, maximum value is 0.26150, mean value is 0.0736110 and a standard deviation is
0.05550494. The minimum value of nature of industry is -0.09614, maximum value is 0.21931,
mean value is 0.0062443 and a standard deviation is 0.03833106. The minimum value of
ineffective monitoring is 0.20000, maximum value is 0.60000, mean value is 0.4084806 and
standard deviation is 0.09001462. The minimum value of rationalization is -0.13128, maximum
value is 0.32609, mean value is 0.0360176 and the standard deviation is 0.07363789.
Meri Kristianti, Carmel Meiden 203

Table 2. Frequency of DCHANGE

Valid Cumulative
Frequency Percent Percent Percent
Valid 0 85 70.8 70.8 70.8
1 35 29.2 29.2 100.0
Total 120 100.0 100.0
Source: Data results, 2021
Based on table 2, it shows that the total sample of companies is 120. Companies that do
not have a change of director amounted to 85 companies or 70.8%. While the company that has
a change of director amounted to 35 companies or 29.2%
Table 3. Frequency of Fraud
Valid Cumulative
Frequency Percent Percent Percent
Valid 0 47 39.2 39.2 39.2
1 73 60.8 60.8 100.0
Total 120 100.0 100.0
Source: Data results, 2021
Based on table 3, it shows that the total sample of companies amounted to 120.
Companies that are not indicated fraud amounted to 47 companies or 39.2%. While the
companies indicated fraud amounted to 73 companies or 60.9%.
Logistic Regression Analysis
a. Overall Model Test Results
This test is a useful test for assessing the overall number of hypothesized models and
research data. Likelihood L of the model is the probability that the hypothesized model
describes the inputted data (Ghozali, 2018: 332). Table 4 showed a decrease where the final
value of -2 Log Likelihood of 77,669 was lower than the initial value of -2 Log Likelihood of
160,677, then don't refuse H0 . Which means that the hypothesized model is fit with the data.

Table 4. Overall Model Fit Test Result

Iteration History
Iteration -2 Log Likelihood
Block 0 160.677
Block 1 77.669
Source: Data results, 2021
b. Regression Model Feasibility Test Results
According to Ghozali, 2018: 333) Hosmer and Lemeshow's test is a test to determine
whether empirical data is suitable or in accordance with the model. Based on table 5,
showing the Chi-square value 10,837 with a significant value of 0.211 which is greater than
0.05 then don't refuse H0 . This means the model matches the data so the regression model
is able to explain the data.
Table 5. Hosmer and Lemeshow Test Results
Step Chi-square Df Sig.
1 10.837 8 .211
Source: Data results, 2021
c. Coefficient of Determination Test Results (Nagelkerke's R square)
204 Meri Kristianti, Carmel Meiden

Nagelkerke's R square test is a test for finding out the magnitude of variability of
dependent variables that can be explained by independent variable variability (Ghozali,
2018: 333). Based on table 6 of Nagelkerke's R square test results of 0.677 which is indicates
the effect of the independent variable on the dependent variable is 67.7%.
Table 6. Nagelkerke's R Square Test Results
-2 Log Cox & Snell R Nagelkerke R
Step likelihood Square Square
1 77.669a .499 .677
Source:
Data results, 2021

d. Classification Matrix Test Results

According to (Ghozali, 2018: 334) the classification matrix test is a useful test for
calculating correct and incorrect estimation values. Based on table 7, it shows that the
predictive power of the regression model in predicting the probability of the model's
prediction rate is 85.8%, of which 90.4% of fraud and 78.7% of non-fraud have been able to
be predicted by the model. The predictive power of the regression model to predict the
possibility of fraudulent financial statements is 90.4%. This shows that there are 66
companies predicted to make fraudulent financial statements from a total of 73 companies
that make fraudulent financial statements. The predictive power of the model of companies
that are indicates not to have committed which means there are 37 companies out of a total
of 47 companies that do not commit fraudulent financial statements. So that the overall
classification accuracy is 85.8%.
Table 7. Classification Matrix Test Results
Predicted
MSCORE Percentage
Observed 0 1 Correct
Step 1 MSCORE 0 37 10 78.7
1 7 66 90.4
Overall Percentage 85.8
Source: Data results, 2021
e. Logistics Regression Coefficient Significance Test Results
This test is useful for showing how far the influence of one variable is explained or
independent individually in explaining the variation of dependent variables (Ghozali, 2018:
98).
Table 8. Coefficient Logistic Regression Test Results
B S.E. Wald df Sig. Exp(B)
Step ACHANGE .911 2.629 .120 1 .729 2.487
1 a LEV .384 2.288 .028 1 .867 1.468
OSHIP -.885 1.373 .415 1 .519 .413
ROA 2.049 6.914 .088 1 .767 7.757
REC 45.610 15.517 8.640 1 .003 6.427E+19
BDOUT -2.866 3.231 .786 1 .375 .057
TATA 43.823 9.880 19.676 1 .000 1.077E+19
DCHANGE -.952 .669 2.028 1 .154 .386
Constant .593 1.787 .110 1 .740 1.810
Meri Kristianti, Carmel Meiden 205

Source: Data results, 2021

Based on table 8, the equation of the logistic regression model is processed as follows:
M-Score = 0.593 + 0.911 ACHANGE + 0.384 LEV – 0.885 OSHIP + 2.049 ROA + 45.610 REC
– 2.866 BDOUT + 43.823 TATA – 0.952 DCHANGE
Discussion
1. The Effect of Financial Stability on The Possibility of Fraudulent Financial Statements
Test results from financial stability variable as proxied by ACHANGE have a coefficient
value of 0.911 with a significance level of 0.364 (0.729 ÷ 2) greater than 0.05 so don't refuse H0 .
That means that it is not proven that financial stability has a significant positive effect on the
possibility of fraudulent financial statements, so H1 is rejected. This shows that when the
financial condition of a company is unstable, the management tends to come under pressure to
make fraudulent financial statements, one of which is asset manipulation. The insignificant
results in this study is because there is growth in assets or funding from third parties. This study
has results consistent with (Norbarani & Rahardjo, 2012; Sunardi & Amin, 2018) however, the
results of this study contradict the research conducted by (Sihombing & Rahardjo, 2014) which
showed that the variable financial stability has a significant positive effect on the possibility of
fraudulent financial statements.
2. The Effect of External Pressure on The Possibility of Fraudulent Financial Statement
Test results from external pressure variable as proxied by LEV have a coefficient value of
0.384 with a significance level of 0.433 (0.867 ÷ 2) greater than 0.05 so don't refuse. H0 . That
means that it is not proven that external pressure have a significant positive effect on the
possibility of fraudulent financial statements, so H2 is rejected. This shows that outside pressure
to meet the expectations and requirements of third parties that are generally caused by debt
owned by the company has an insignificant positive influence on financial statement fraud. This
happens because companies that have a large leverage ratio in this study will not necessarily
commit fraud that can be affected by a good surveillance system so that the effect of external
pressure on fraudulent financial statements is not significant. This study has results consistent
with research conducted by (Annisya et al., 2016) however, the results of this study contradict
the research conducted by (Sihombing & Rahardjo, 2014) which showed that external pressure
variables have a significant positive effect on the possibility of fraudulent financial statements.
3. The Effect of Personal Financial Need on The Possibility of Fraudulent Financial Statement
Test results from personal financial need variable as proxied by OSHIP have a coefficient
value of -0.885 with a significance level of 0.259 (0.519 ÷ 2) greater than 0.05 so don't refuse H0 .
That means it is not proven that personal financial need has a significant positive effect on the
possibility of fraudulent financial statements, so that H3 is rejected. Based on the test results
showed that the ownership of shares owned by company insiders, both directors, board of
commissioners, and other management did not positively affect the possibility of financial
statement fraud. This can happen due to the low average shareholdings held by company
insiders in this sample of research so that insiders who own company shares cannot significantly
influence decisions for their own benefit. This study is consistent with the results of research
conducted by (Yulistyawati et al., 2019) however, the results of this study contradict the results
of research conducted by (Sari & Lestari, 2020) which showed that personal financial need
variables have a significant positive effect on the possibility of fraudulent financial statements.

4. The Effect of Financial Targets on The Possibility of Fraudulent Financial Statements

The test results of the target financial variable as proxied by ROA have a coefficient value
of 2,049 with a significance level of 0.383 (0.767 ÷ 2) greater than 0.05 so don't refuse H0 . That
means it is not proven that the target financial variable has a significant positive effect on the
206 Meri Kristianti, Carmel Meiden

possibility of fraudulent financial statements, so that H4 is rejected. This can happen because
the company management in this study will not necessarily manipulate profits on financial
statements to achieve financial targets. In addition, the increase in financial targets it can be not
considerable pressure because the increase in ROA accompanied by improvements in the quality
of company operations such as modernization of information systems, recruitment of potential
workers, effective supervision and appropriate board of director policies in solving problems.
This study has results consistent with research conducted by (Annisya et al., 2016) however, the
results of this study contradict the research conducted by (Sunardi & Amin, 2018) which showed
that the target financial variables had a significant positive effect on the possibility of fraudulent
financial statements.
5. The Effect of Nature of Industry on The Possibility of Fraudulent Financial Statement
Test results from nature of industry variable as proxied by REC have a coefficient value
of 45,610 with a significance level of 0.0015 (0.003 ÷ 2) that is smaller than 0.05 so refuse H0 .
That means it is proven that nature of industry has a significant positive effect on the possibility
of fraudulent financial statements, so that H5 is accepted. This shows that the nature of the
industry projected by the average change in the company's receivables to the company's sales
has a positive and significant effect on the possibility of financial statement fraud. An increase
in the amount of a company's receivables from the previous year significantly or
disproportionately to sales may be an indication of manipulation of financial statements. This
study is consistent with the results of research conducted by (Sihombing & Rahardjo, 2014)
however, the results of this study contradict the research conducted by (Sari & Lestari, 2020)
which showed that the variable nature of industry has a significant negative effect on the
possibility of fraudulent financial statements.
6. The Effect of Ineffective Monitoring on The Possibility of Fraudulent Financial Statements
Test results from ineffective monitoring variable as proxied by BDOUT have a coefficient
value of -2,866 with a significance level of 0.187 (0.375 ÷ 2) greater than 0.05 so don't refuse H0 .
That means it is not proven that ineffective monitoring has a significant positive effect on the
possibility of fraudulent financial statements, so that H6 is rejected. This can happen because
the existence of an independent board of commissioners in the company can provide assurance
that the supervision of the company will be more effective and objective away from the
intervention of certain parties. The smaller the ratio, the lower the effectiveness of corporate
supervision, the higher the likelihood of fraud. This study is consistent with research conducted
by (Sihombing & Rahardjo, 2014) however, the results of this study contradict the research
conducted by (Damayanti & Suryani, 2019) which showed that ineffective monitoring variable
have a significant positive effect on the possibility of fraudulent financial statements.
7. The Effect of Rationalization on The Possibility of Fraudulent Financial Statements
The test results of the rationalization variable as proxied by TATA have a coefficient value
of 43,823 with a significance of 0.000 which is less than 0.05 so refuse H0 . That means it is proven
that rationalization has a significant positive effect on the possibility of fraudulent financial
statements, so H7 is Accepted. This means that the principle of accrual is utilized by the
management of the company to manipulate profits which is influenced by the rationalization of
management in decision making. Accrual arises due to the existence of rules, assumptions or
accounting policies such as depreciation, and others. Making decisions about accounting rules is
of course made by management to change income as desired. This study is consistent with (Sari
& Lestari, 2020) however, the results of this study contradict the research conducted by
(Agusputri & Sofie, 2019) which showed that the rationalization variable have a negative effect
on the possibility of fraudulent financial statements.
Meri Kristianti, Carmel Meiden 207

8. The Effect of Capability on The Possibility of Fraudulent Financial Statements

The test results of the capability variable as proxied by DCHANGE have a coefficient value
of -0.952 with a significance level of 0.154 greater than 0.05 so don't refuse H0 . That means that
it is not proven that capability has a significant effect on the possibility of fraudulent financial
statements, so that H8 is rejected. This indicates that changes in the board of directors in the
company do not trigger fraudulent financial statements that can be caused by the existence of
an effective supervision system from the board of commissioners. Changes to board members
can also indicates the company wants changes and progress in the company by recruiting
directors who are considered more competent than previous directors. This study is consistent
with (Umar et al., 2020) however, the results of this study contradict the research conducted
(Sunardi & Amin, 2018) which showed that the capability variable effect the possibility of
fraudulent financial statements.

CONCLUSION

Based on the results of the tests conducted, the conclusions obtained in this study that:
1) It is not proven that financial stability, external pressure,personal financial need, financial
target, ineffective monitoring have a positive effect on the possibility of fraudulent financial
statements. 2) It is proven that the nature of industry and rationalization have a positive effect
on the possibility of fraudulent financial statements. 3) It is not proven that capability has an
effect on the possibility of fraudulent financial statements.
Some suggestions based on the results of research that have been considered, for the
company, it is expected that the company can take precautions by improving the surveillance
system, conducting segregation of duties and making internal auditor changes. For companies
that are indicated the possibility of fraudulent financial statements is expected to be an
evaluation material, especially in reviewing accounts related to receivable ratios as proxies of
nature of industry and the ratio of total accruals to total assets as proxy rationalization more
thoroughly.
For practitioners such as investors and potential investors, with this research is expected
to be more careful in assessing the company's financial statements before making decisions or
investing. For further researchers, it is expected that research with similar topics can next use a
sample of different or broader studies (not limited to just one industry only), use other proxies
that are more accurate, use new variables in fraud analysis such as fraud pentagon, extend the
research period in order to generalize the results of the study in explaining the influence of fraud
risk factors.

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