Research Article: 2022 Vol: 21 Issue: 3
Gunarianto, Widyagama University
Endah Puspitosarie, Widyagama University
Muchlis Mas'ud, Widyagama University
Citation Information: Gunarianto, Puspitosarie, E., & Mas'ud, M. (2022). The role of pressure, opportunity and rationalization on fraud financial statements: A case study of banking companies on the Indonesia stock exchange. Academy of Strategic Management Journal, 21(3), 1-11.
Financial statement fraud is the type of fraud that occurs with the least frequency but causes large average losses. The purpose of this study was to determine the effect of pressure, opportunity, and rationalization on fraudulent acts of financial statements proxies by earnings management. The population used in this study is banking sector companies listed on the Indonesia Stock Exchange for the period 2019-2021 with a sampling technique based on criteria, 1. Banking sector companies go public, 2. Companies that have deposited their annual financial statements; and 3. Banking entities that only earn profits in the period 2019 to 2021. The total sample is 75 banking and financial service companies. Hypothesis testing was done by logistic regression using SPSS software. The results showed that simultaneously pressure, opportunity, and rationalization effect d fraudulent financial statements in banking sector companies. Partially, the pressure factor as measured by the average financial stability, external pressure and financial targets has a significant effect on the detection of financial statement fraud. The opportunity factor as measured by the average nature of the industry and effective monitoring is proven to have a significant effect on the detection of financial statement fraud. Meanwhile, the rationalization factor measured by total asset accruals has no effect on the detection of fraudulent financial statements.
Pressure, Opportunity, Rationalization, Earnings Management, Fraud.
Fraud is an act of fraud or error by a person or organization that results in the loss of an individual or entity. In a company or corporation, fraud is a concern for stakeholders, regulators and auditors (Cheng et al., 2021a). Fraud is not an easy thing to detect, and its detection requires knowledge of the basics of fraud (Karajian & Ullah, 2021). One form of fraud is financial statement fraud, and this has a material impact and results in misinformation to its users (Hou et al., 2021). In addition, other scholar argues that financial statement fraud is a business that is carried out intentionally to provide incorrect information to users of financial statements.
Several researchers have carried research on financial statement fraud out Luo et al. (2020) find that financial stability and family firms have a positive effect on financial statement fraud. In addition, external pressure and total accruals have a negative effect on fraudulent financial statements. The nature of the industry, effective monitoring, changes in auditor, changes in director, the proportion of independent commissioners, and the frequent number of CEO's pictures was not found to have any effect on the incidence of financial statement fraud. Gam et al. (2021) stated that the pressure represented by the financial target variable and rationalization with the change in auditors variable had an effect on financial statement fraud. The opportunity represented by the ineffective monitoring variable and the ability with the replacement of directors’ variable has no effect on financial statement fraud. Cheng et al. (2021b) using the F-Score model for mining companies, found that Financial Stability has a positive effect on financial statement fraud. Financial targets, personal financial needs, external pressure and effective monitoring, have no effect on fraudulent financial statements.
Wei et al. (2021) stated that the frequency of the industrial sector that mostly committed financial statement fraud was the banking and financial services sector. However, the banking industry has stricter regulations than other industries. Bank Indonesia uses financial statements as the basis for determining health status or not. Therefore, managers can manipulate their financial statements so that the company meets these criteria (Chen et al., 2021c). This illustrates that the banking and financial services industry has the potential for fraudulent financial statements, so the researchers chose the banking sector as the object of research in this study.
In previous studies, there has been no discussion of the pressure, opportunity, and rationalization factors simultaneously for the detection of fraudulent financial statements in banking and financial service companies. Therefore, it is necessary to conduct research related to the pressure factor based on financial stability, external pressure, and financial targets, and the opportunity factor based on the nature of the industry and ineffective monitoring, as well as the rationalization factor based on actual assets to detect fraudulent financial statements of banking companies.
Wang et al. (2017) using the F-scores model found that the pressure variable on the financial target proxy has an effect on the tendency of financial statement fraud. Meanwhile, the proxies of financial stability and external pressure have no effect on the tendency of fraudulent financial statements. The opportunity variable on the proxy for the ineffectiveness of supervision effect s the tendency of financial statement fraud. For proxies, the nature of the industry has no effect on the tendency of fraudulent financial statements. The rationalization variable on the audit change proxy and the capability variable on the change of director’s proxy have no effect on the tendency of financial statement fraud.
Osegi & Jumbo (2021) Using fraud firms and non-fraud firms found that the elements of pressure (financial stability, external pressure, and personal financial need) had a positive effect, and the element of rationalization (auditor switching) had a positive effect while the element of opportunity (organizational structure) has a negative effect. Meanwhile, elements of financial targets, the nature of the industry, and ineffective monitoring have no effect on fraudulent financial reporting.
Othman et al. (2015) use the F-Score model and state that Financial Stability has a positive effect on financial statement fraud. The four variables used, namely effective monitoring, personal financial need, external pressure and financial targets, have no effect on fraudulent financial reporting.
The population used is banking and financial services sector companies listed on the Indonesia Stock Exchange for the period 2019 to 2021. This study uses a sample of 75 samples of banking sub-sector companies with the following criteria: 1. publicly listed banking sector companies, 2. Companies that have submitted the annual financial statement; and 3. Banking entities that only earn profits in the period 2019 to 2021.
Data analysis in this study used descriptive statistical analysis and logistic regression analysis. Logistic regression is used because the dependent variable is a dummy and has a nominal scale. Binary logistic regression analysis is a predictive model whose dependent variable is a dichotomous scale, the dichotomous scale in question is the nominal data scale with two categories. In this study, earnings management is carried out by decreasing profits and increasing profits.
Based on the data obtained in the study, data processing was carried out using SPSS, obtained in Table 1 and Table 2.
Table 1 shows the variable of financial statement fraud as measured by earnings management, as much as 92% of fraudulent financial statements by lowering profits, and 8% of the sample committing fraudulent financial statements by increasing profits. This result is consistent with (Zager et al., 2016). Management carries this condition out as an effort to motivate them to get the maximum bonus. Large companies tend to manage earnings. This is done by doing income decreasing when getting high profits. This manipulation avoids the emergence of new regulations from the government, for example, an increase in corporate income tax.
| Table 1 Earnings Management |
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|---|---|---|---|---|---|
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | Lowering Profit | 69 | 92.0 | 92.0 | 92.0 |
| Increase Profit | 6 | 8.0 | 8.0 | 100.0 | |
| Total | 75 | 100.0 | 100.0 | ||
Table 2 shows the Pressure variable based on the asset growth ratio, debt ratio, and return on asset ratio. This means that the average pressure of the companies studied to commit financial statement fraud is 31.07%. This means that there is firm pressure on management always to produce a good performance, causing management to commit financial statement fraud by increasing or decreasing company profits.
| Table 2 Ratio Of Descriptive Statistics |
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|---|---|---|---|---|---|
| N | Minimum | Maximum | Mean | Standart Deviation | |
| Pressure | 75 | 0.14 | 0.53 | 0.3107 | 0.05457 |
| Opportunity | 75 | -0.52 | 4.94 | 0.8801 | 0.96288 |
| Rationalization | 75 | -0.13 | 0.14 | 0.0055 | 0.05541 |
| Valid N (listwise) | 75 | ||||
Opportunity variable based on the ratio of changes in receivables and the ratio of independent commissioners shows a minimum value of 0.52, meaning that the minimum opportunity for the company under study to commit financial statement fraud is 52%, the maximum value is 4.94, which means the maximum opportunity for the company under study to commit financial statement fraud is 4.94%, the average value is 0.88, meaning that the average opportunity of the companies studied to commit financial statement fraud is 88.01%, with a standard deviation of 0.96.
The Rationalization variable based on total asset accruals shows a minimum value of -0.13, meaning that the minimum rationalization of the company studied for fraudulent financial statements is -13%, the maximum value is 0.14, which means the maximum rationalization of the company studied for fraudulent financial statements is 14%, the average value (mean) is 0.0055, so the average rationalization of the companies studied to commit financial statement fraud is 0.55% with a standard deviation of 0.06.
Binary logistic regression analysis is a predictive model of the dependent variable with a dichotomous scale or has a nominal data scale with two categories. In this study, the dependent variable is dummy, namely companies with lower profits and increasing profits categories. The following is an analysis to test the hypothesis in binary logistic regression analysis:
In assessing the overall feasibility of the model, L was transformed to -2LogL. The -2LogL statistic is sometimes called the likelihood ratio two statistic, where 2 is the distribution with degree of freedom (DF) =n-q-1, n is the number of samples; q is the number of independent variables. SPSS output gives two values of -2LogL, namely in Table 3 for models that only include constants without including independent variables (block 0), and for models with constants and independent variables (block 1). The decrease in the value of -2LogL from block 0 to block one show the null hypothesis is accepted, meaning that the overall model is feasible or fits the data.
| Table 3 Overall Model Fit Test (Block 0) Iteration Historya,B,C |
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|---|---|---|---|
| Iteration | -2Log likelihood | Coefficients | |
| Constant | |||
| Step 0 | 1 | 45.795 | -1.680 |
| 2 | 42.004 | -2.262 | |
| 3 | 41.816 | -2.430 | |
| 4 | 41.815 | -2.442 | |
| 5 | 41.815 | -2.442 | |
Table 4 shows the -2LogL value of 41.815, when compared to the value of 2 tables at DF=68 (75-6-1) of 88.250, meaning that the model before the independent variable was entered was already fitted with the data because the calculated -2LogL value (41.1815) was smaller than two tables (88,250). Table 5 (block 1) shows the value of -2LogL of 29,709, which is greater than 2 table at DF=68 (75-6-1) of 88,250, meaning that the model after the independent variable is entered the data. Assess the feasibility of the overall model, it can be calculate the difference between the calculated values of -2LogL block 0 and block 1, and the calculation is shown in Table 5.
| Table 4 Overall Model Fit Test (Block 1) Iteration Historya,B,C,D |
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|---|---|---|---|---|---|---|
| Iteration | |
Coefficients | ||||
| Constant | Pressure | Opportunity | Rationalization | |||
| Step 1 | 1 | 42.413 | -0.381 | -3.685 | -0.196 | 3.394 |
| 2 | 34.363 | 0.860 | -9.137 | -0.552 | 8.543 | |
| 3 | 30.969 | 2.531 | -15.085 | -1.186 | 15.131 | |
| 4 | 29.842 | 3.871 | -19.972 | -1.808 | 20.381 | |
| 5 | 29.711 | 4.506 | -22.475 | -2.059 | 22.796 | |
| 6 | 29.709 | 4.604 | -22.876 | -2.095 | 23.176 | |
| 7 | 29.709 | 4.606 | -22.884 | -2.095 | 23.184 | |
| 8 | 29.709 | 4.606 | -22.884 | -2.095 | 23.184 | |
Table 5 shows a positive difference of 12,106, and this indicates that there is a decrease in the calculated value of -2LogL, so it can be concluded that the overall model fits the data.
| Table 5 Overall Model Fit Test |
||
|---|---|---|
| --2LogL0 | -2LogL1 | (-2LogL0--2LogL1) |
| 41.815 | 29.709 | 12.106 |
The Goodness of Fit Test
The feasibility test of the regression model was carried out with Hosmer and Lemeshow's Godness of Fit Test. If the statistical significance value of Hosmer and Lemeshow's Godness of Fit Test is less than or equal to a significance level of 5%, it means that there is a significant difference between the model and the observed value, so the model is not feasible, because it cannot predict the observed value. If the significance value of Hosmer and Lemeshow's Godness of Fit Test statistic is more than the 5% significance level, it means that there is no significant difference between the model and the observed value, so the model is said to be feasible because it can predict the observed value. The results of the Goodness of Fit Test are presented in Table 6.
| Table 6 Goodness Of Fit Test |
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|---|---|---|---|
| Step | Chi-square | Df | Sig. |
| 1 | 1.200 | 7 | 0.991 |
Table 6 shows the significant value of Hosmer and Lemeshow's Godness of Fit Test is 0.991, this value is greater than the significance level of 0.05, it can be concluded that the model can predict the observation data, or there is no significant difference between the model and the observed value.
Coefficient of Determination Test
To get the coefficient of determination in the logistic regression model, it can be seen from Cox & Snell's R Square, identical to the value of R2 in multiple regression. Nagelkerke R Square is a modification of the Cox & Snell coefficient to ensure that its value varies from 0 to 1. This is done by dividing Cox & Snell R square by its maximum value. The value of Nagelkarke R Square can be interpreted as the value of R2 in multiple regression (Novira et al., 2018). The results of the coefficient of determination test are shown in Table 7.
| Table 7 Coefficient Of Determination Test |
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|---|---|---|---|
| Step | -2 Log likelihood | Cox & Snell R Square | Nagelkerke R Square |
| 1 | 29.709a | 0.149 | 0.349 |
In Table 8, the Nagelkerke R Square value is 0.349, meaning that the ability of the independent variables, namely pressure, opportunity, and rationalization in explaining the dependent variable, namely financial statement fraud, is 34.9%, while the remaining 65.1% is explained by other factors outside this research model.
| Table 8 Classification Matrix Test |
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|---|---|---|---|---|---|
| Observed | Predicted | ||||
| FSF | Percentage Correct | ||||
| Lowering Profit | Lowering Profit | ||||
| Step 1 | FSF | Lowering Profit | 68 | 1 | 98.6 |
| Increase Profit | 6 | 0 | 0 | ||
| Overall Percentage | 90.7 | ||||
Classification Matrix Test
Table 8 shows predictions of financial statement fraud based on the earnings management—classification table in the classification matrix test.
Table 8 shows the predictive power of the model, the number of samples experiencing financial statement fraud as proxies by earnings management with category 0 or lowering earnings, there are 69 samples with a predictive value of 68 samples actually doing earnings management by lowering earnings, and one company is predicted to carry out earnings management by reducing earnings. Increase profits but perform earnings management by lowering profits. So the value of the accuracy of the model in measuring financial statement fraud as proxies by earnings management is category 0 or lowers profits by 98.6%.
For the number of samples experiencing financial statement fraud as proxies by earnings management with category one or increasing profits, there are six samples with a predictive value of 0 companies actually doing earnings management by increasing profits, and six companies predicted to do earnings management by lowering profits but doing earnings management by increasing profits. So the value of the accuracy of the model in measuring fraudulent financial statements as proxies by earnings management with category one or increasing profits by 0%. The overall accuracy in predicting the power of the model is 90.7%.
Wald Test
Test the significance of the partial effect in binary logistic regression can be done with the results of the Wald Test. In the Wald test, hypothesis testing is carried out by comparing the significance value with a significance level of 5%, as shown in Table 9.
| Table 9 Wald Test |
|||||||
|---|---|---|---|---|---|---|---|
| B | S.E. | Wald | Df | Sig. | Exp(B) | ||
| Step 1a | Pressure | -22.884 | 11.328 | 4.081 | 1 | 0.043 | 0.000 |
| Opportunity | -2.095 | 1.059 | 3.915 | 1 | 0.048 | 0.123 | |
| Rationalization | 23.184 | 12.290 | 3.559 | 1 | 0.059 | 117.852 | |
| Constant | 4.606 | 3.032 | 2.307 | 1 | 0.129 | 100.045 | |
Table 9 shows that the significance value of the pressure variable is 0.043. This value is smaller than the 5% significance level, so that pressure has an effect in detecting fraudulent financial statements. The significance value of the opportunity variable is 0.048, and this value is smaller than the 5% significance level so that the opportunity has an effect in detecting financial statement fraud, and the rationalization variable significance value is 0.059 where this value is greater than the 5% significance level so that the rationalization variable has no effect on fraud detection—financial statements.
Omnibus Test
Simultaneous significance test aims to determine the overall effect of the independent variable on the dependent variable. This test uses the Omnibus Test, see Table 10.
| Table 10 Omnibus Tests Of Model Coefficients |
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|---|---|---|---|---|
| Chi-square | DF | Sig. | ||
| Step 1 | Step | 12.107 | 3 | 0.007 |
| Block | 12.107 | 3 | 0.007 | |
| Model | 12.107 | 3 | 0.007 | |
Table 10 shows a significance value of 0.007. This value is smaller than the 5% significance level, meaning that the independent variables (Pressure, Opportunity, and Rationalization) simultaneously affect the detection of financial statement fraud.
The Role of Pressure on the Detection of Fraudulent Financial Statements
Based on the results of binary logistic regression analysis, it shows that the odds ratio value of the pressure variable as measured by the average of financial stability, external pressure, and the financial target has a significance value of 0.043, which is smaller than the 5% significance level. This shows that pressure has a significant effect on the detection of fraud in financial statements, so the hypothesis that pressure affects the detection of fraud in financial statements is accepted.
The results are in line with research conducted by Shen et al. (2021) which found that pressure has a significant effect on financial statement fraud. The pressure experienced by the company will assist the auditor in detecting fraudulent financial statements because if the company's pressures as financial stability, external pressure, and the company's financial targets, decrease, it will increase the occurrence of financial statement fraud.
The asset growth ratio measures financial stability in this study, showing a negative coefficient. This is because of a decrease in asset value in the sample companies, which causes financial stability to decline and affects the company's asset growth ratio.
Pressure from outsiders as measured by the debt ratio results in a tendency for companies to commit financial statement fraud with low debt ratios because creditors currently do not consider the amount of debt generated but because there are other considerations, such as the high and low free cash flow of the company and there is a good relationship between the company and creditors. Chen et al. (2021a) stated that many companies prefer to issue shares again to get additional business capital without having to enter new debt agreements, which causes the company's debt burden to become greater and the company's leverage value to be lower.
This negative effect can also be caused because banking companies have a better level of supervision by establishing a whistleblowing system. This system is to control and monitor management actions so that even though management faces increasing pressures as financial stability, namely asset growth ratios, external pressures as debt ratios, and financial targets as asset return ratios, the company will be more careful so that it can reduce the risk of fraudulent financial statements. This refers to regulations in Indonesia, namely OJK regulation no. 39/POJK.03/2019 concerning the Implementation of Anti-Fraud Strategy for Commercial Bank. This regulation is used to control the risk of fraud, and banks are required to implement risk management under the provisions, at least containing strengthening of the active supervision of the board of directors and the board of commissioners, aspects of policies and procedures, aspects of organizational structure and accountability, as well as aspects of control and monitoring.
The Role of Opportunity in Detecting Fraudulent Financial Statements
The results of the analysis show that the odds ratio value of the Opportunity variable (X2) as measured by the average of the nature of the industry and effective monitoring is 0.123 with a beta value of 2.095 and a significance value of 0.048, which is smaller than the 5% significance level. This shows that opportunity has a significant affect on the detection of fraud in financial statements, so the hypothesis which states “Opportunity affects the detection of fraud in financial statements” are accepted. The results of this study are in line with research conducted by Craja et al. (2020) which found that opportunity affects financial statement fraud. The opportunity possessed by the company's management will assist the auditor in detecting fraud in the company's financial statements. If the company's opportunity is in the form of industrial nature and the effectiveness of company supervision decreases, it will increase the risk of fraud in the financial statements.
The negative effect in the results is because of opportunity in the industry's nature as measured by the ratio of changes in the company's receivables, the sample in this research is banking companies whose main activity is providing credit to the public so that companies have large receivables and will increase every year (Agustia et al., 2020), meaning that even though the receivables of banking companies continue to increase, it will actually make the company more careful in order to maintain the trust of its debtors and creditors.
The results also support the opportunity as effective supervision that will reduce the occurrence of fraudulent financial statements. In this study, the effectiveness of supervision is measured by the ratio of the independent board of commissioners which in banking companies have met the formal requirements of the Indonesia Stock Exchange, which states: 1) In the event that the Board of Commissioners consists of two members of the board of commissioners, one of them is an independent commissioner; 2). If the Board of Commissioners consists of over two members of the Board of Commissioners, the number of Independent Commissioners must be at least 30% of the total members of the Board of Commissioners (Suh & Shim, 2020). The effectiveness of the company's supervision can be seen from the ratio of the independent board of commissioners, the higher the ratio of the independent board of commissioners of a company, the more effective the level of supervision of a company so that it will reduce the risk of fraudulent financial statements (Wasiaturrahma et al., 2020).
The Role of Rationalization in Detecting Fraudulent Financial Statements
Based on the results of binary logistic regression analysis, it shows that the odds ratio value of the rationalization variable (X3) as measured by the Total Accrual Assets ratio is 11.72 with a beta value of 23,184 and a significance value of 0.059, which is greater than the 5% significance level. This shows that the hypothesis which states “rationalization affects the detection of financial statement fraud” is rejected, meaning that rationalization has no effect on the detection of financial statement fraud. The results strengthen the results of the research (da Silva Azevedo et al., 2021). The results of his research found that rationalization, as measured by the ratio of total assets to accruals, had no effect on fraudulent financial statements. Accrual is an accounting method in which receipts and disbursements are recognized or recorded when transactions occur, not when cash for those transactions is received or paid. Accruals are accounting products that can have a relatively fixed amount from year to year. This is because the related accounting rules have not changed. Muthukannan et al. (2021) states that changes in accruals that occur result from excessive use of management policies, and if at the same time management also has incentives or motives to manipulate earnings, changes in accruals that occur are considered as earnings manipulation by management.
The concept of discretionary accruals also means that management can manipulate income by recording when transactions occur, even though cash has not been issued or received. This situation is often used to achieve the desired income (Burnett, 2020). However, the results indicate that the use of management policy (discretionary) is not in the high category, meaning that the motive for committing fraudulent financial statements is low. According to Maka et al. (2020), rationalization is the most difficult element to measure because rationalization is an attitude just carried out by management, employees, or the board of commissioners (Wasiaturrahma et al., 2020).
The Role of Pressure, Opportunity and Rationalization Simultaneously on the Detection of Fraudulent Financial Statements
Financial statement fraud is one type of fraud that causes material errors in financial statements. This can be done by intentionally omitting transactions, creating false transactions, incorrect ending balance statements, disclosure of incomplete reports, or the application of incorrect accounting standards. Financial statement fraud often begins with a misstatement or earnings management of the quarterly financial statements that are immaterial but eventually grow into massive fraud and produce materially misleading annual financial statements. Theoretically, the results of the agency theory states that the difference in interests between the agent (company management) and the principal (owner or investor) can cause a conflict of interest which can trigger the company management to commit fraud.
The results of the study illustrate that the pressure measured through the use of financial stability, external pressure, and financial targets has been proven to have a significant effect on fraud detection of financial statements. Opportunity, which is measured through the use of industry variables and effective monitoring, is proven to have a significant effect on fraud detection on financial statements. Meanwhile, rationalization, which is measured through the use of total asset accruals, has not been shown to have a significant effect on fraud detection in financial statements.
Practical Implications
Based on the findings of this study simultaneously, the perspective of pressure, opportunity, and rationalization are proven to have an effect on fraud detection in financial statements.
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Received: 03-Jan-2022, Manuscript No. ASMJ-21-10734; Editor assigned: 06-Jan-2022, PreQC No. ASMJ-21-10734 (PQ); Reviewed: 24-Feb-2022, QC No. ASMJ-21-10734; Revised: 29-Jan-2022, Manuscript No. ASMJ-21-10734 (R); Published: 03-Feb-2022