Analysis of Countercyclical Policy Factors in The Era of the COVID-19 Pandemic in Financial Statement Fraud Detection of Banking Companies in Indonesia

Abstract

This research is motivated by the application of countercyclical policy, such as credit restructuring, by banking companies during the COVID-19 pandemic. The credit restructuring that the banks carried out had the potential to affect their financial performance and increase the tendency of financial statement manipulations. The purpose of this study is to review the significance of the changes in banks’ financial performance before and after the implementation of the credit restructuring, as well as its relationship to the potential for fraudulent financial statements. The population in this study was the financial sector companies listed on the Indonesia Stock Exchange in 2020, a total of 105 companies. The 96 samples consisted of 32 banking companies in the commercial banking category that have implemented countercyclical policies in the form of credit restructuring during the COVID-19 pandemic. The research method was a quantitative method using secondary data obtained from the official website of the Indonesia Stock Exchange and the official websites of the related companies. The results showed no significant change in the banks’ financial performance. Although the credit restructuring had an impact, such as a decrease in their profits, it did not encourage them to manipulate their financial statements.

1. Introduction

The current state of the COVID-19 pandemic is one of the factors causing fraud to companies. The COVID-19 pandemic has prompted the government to make various efforts to prevent the spread of the virus and to create regulations aimed at optimizing company performance and supporting economic growth during the pandemic. Some of these regulations have the potential to have a direct impact on the continuity of the companies’ operations so special care needs to be taken so that the restructuring policies implemented by banking companies do not open gaps for fraudulent financial statements and cause companies’ financial statements to be unreliable [1]. The implementation of credit restructuring during the ongoing COVID-19 pandemic has attracted the attention of economists, banking business players, and the Financial Services Authority as a regulator. The President Director of PT Bank Rakyat Indonesia (Persero) Tbk, Sunarso, stated that future credit restructuring would impact banking liquidity and operating income [2]. The relaxation carried out caused a delay in the payment of principal loans, thereby reducing bank liquidity. In addition, the relaxation also caused interest payments to be delayed and impacted income [3]. The President Director of PT Bank Central Asia Tbk (BCA) Jahja Setiaatmadja also stated that credit restructuring during the COVID-19 pandemic challenged banks to be more observant and selective in providing restructuring, given the liquidity risks that could arise [4]. The same thing was said by Center of Reform on Economics (CORE) economist Yusuf Rendy Manilet who added that credit restructuring could have a negative impact on banks [5]. Yusuf assessed that the restructuring could affect the banks’ income because they have the potential to experience a temporary delay in income from lending [6] (As a regulator, the Chief Executive of the Banking Supervision of the Financial Services Authority (OJK, Heru Kristiyana) also told banks to be more aware of credit risk during the COVID-19 pandemic. He said that Loan at Risk (LaR) as an indicator of bank lending risk in early 2021 was still quite large so banks should be more vigilant and careful in their management [7].

The financial sector plays an essential role in triggering the economic growth of a region [7]. The financial sector can affect economic growth through two channels: capital accumulation (physical capital and human capital) and technological innovation pathways [8]. The two paths are the primary sources of long-term economic growth developed in the theoretical literature on economic growth [9]. The strategy and role of the government in the regional economy include increasing economic growth and regulating the provision of public goods (allocations), reducing inflation and unemployment (stabilization), and implementing equity (social justice) or distribution [10]. These roles are carried out through the real sector (goods sector) and the monetary sector (financial sector). The role of the monetary sector can be understood through the banking industry, which has an essential role in the economy as an intermediary institution that channels public funds into investment in productive assets that will encourage real sector productivity, capital accumulation, and growth in aggregate output [11]. The role of credit is significant in encouraging the role of the real sector as a derivative of fiscal and monetary policies that can encourage regional economic acceleration in the real sector [12]. Based on regional economic growth indicators and credit allocation, there are indications that credit acts as a stimulator of economic growth [13].

Banking regulations in Indonesia are carried out by stipulating the laws on banking. The objective of the laws on banking is to protect the banking industry in the face of risk, which also means protecting customers and the economy from the failures of the processes and procedures that can impact the overall financial system. The laws on banking are (1) RI Law No. 7 of 1992 concerning banking as amended by the Law of the Republic of Indonesia No. 10 1998 and (2) the Law of the Republic of Indonesia No. 23 of 1999 concerning BI amended by the Law of the Republic of Indonesia No. 3 of 2004. There are several reasons regulations are needed for banking: (1) ratio of debt to capital (leverage); (2) capital; (3) insolvency; (4) the role of the Central Bank as the lender as a last resort; (5) financial stability; (6) monetary stability; and (7) international financial liberalization (Financial Services Authority, 2020). Competition between banks and the innovation of financial products are among the scope of the Financial Services Authority’s bank regulation and monitoring authority. Under this authority, the focus is to ensure the optimum functioning of Indonesia’s banking system as an institution (1) of public trust in relation to funding and distributing funds; (2) for the execution of monetary policy; and (3) that plays a role in helping economic growth and equity. The aim is to maintain a healthy and sound banking system, as well as individual banks, which can protect the public interest and be beneficial to the national economy. To achieve this goal, the approaches used are (1) deregulation; (2) prudential banking; and (3) self-regulatory banking.

Financial Services Authority Regulation (POJK) Number 11/POJK.03/2020 concerning the National Economic Stimulus as a countercyclical policy to alleviate the impact of the spread of COVID-19, states that banks can implement policies that support economic growth stimulus for debtors affected by the spread of COVID-19. These policies include determining asset quality and restructuring credit or financing policies. The implementation of the restructuring policy requires special attention from the banks in order to remain able to minimize credit risk, such as the occurrence of a surge in bank non-performing loans (NPL), when the policy ends or when the impact of the spread of COVID-19 ends so as not to trigger conditions that encourage business leaders to commit fraud in their reports [14]. The importance of the role of financial statements as a source of information on the company’s financial position makes it necessary to take steps to detect fraud in financial statements. Fraud detection can be carried out by examining the components of the financial statements and reviewing them further if there are significant changes, especially after the implementation of countercyclical policies. One way to detect fraudulent financial statements is to use statistical techniques such as the Beneish M-score model [15]. Study on the quantitative differences between public companies that engage in financial manipulations and companies that do not, using eight financial ratios, namely days’ sales in receivables index (DSRI), gross margin index (GMI), asset quality index (AQI), sales growth index (SGI), depreciation index (DEPI), sales general and administrative index (SGAI), leverage index (LVGI), and total accruals to total assets (TATA) [16]. By reviewing the significance of changes in a company’s financial performance in the period before and after the implementation of countercyclical policy, assessors were able to determine whether the credit restructuring implemented by a company had an impact on its financial performance, which could trigger some companies to manipulate their financial statements and which in turn would be detected using the Beneish M-score method [17]. The method is deemed to be effective to detect the propensity of financial statement misreporting and provide more observations in the analysis.

Therefore, this study is expected to be useful for auditors so that they can pay more attention to current issues and economic phenomena and their potential to impact the related companies [18,19]. In addition, this research is also expected to be useful for company management, specifically the banking sub-sector, in conducting restructuring to maximize the company’s risk management performance and minimize the negative impact of restructuring that may arise [3,20].

This is the first study to analyze the countercyclical policy factors in the era of COVID-19 in the detection of fraudulent financial statements by banking companies. The contribution of this research is expected to provide benefits for researchers or auditors and increase the focus on the analysis of the components of financial statements that have the potential to be rigged, especially following the enactment of the restructuring policies [21]. Thus, detecting fraudulent financial statements could be more accurate and on target, which is expected to minimize their occurrence [22]. In addition, this research is expected to be a reference for the government as well as authorities within companies (management) for creating new regulations, considering that these regulations, such as countercyclical policies, have the potential to encourage fraudulent financial statements, [23]. This research is also expected to become a reference material for the development of future studies related to countercyclical policies and the detection of fraud in financial statements [24,25].

Based on the background of the research described above, the researcher tested and analyzed the countercyclical policy factors on company performance and detected financial statement fraud using the Beneish M-Score Model method. The formulation of the problem in this study are:

• How is the change in financial performance based on profitability ratios after the implementation of the countercyclical policy in bank sub-sector companies listed on the Indonesia Stock Exchange?
• How is the change in financial performance based on the liquidity ratio after the implementation of the countercyclical policy in bank sub-sector companies listed on the Indonesia Stock Exchange?
• How is the change in financial performance based on the solvency ratio after the implementation of the countercyclical policy in bank subsector companies listed on the Indonesia Stock Exchange?
• How many bank subsector companies are listed on the Indonesia Stock Exchange which are categorized as manipulator companies based on the calculation of the Beneish M-Score Model?
• How many bank subsector companies are listed on the Indonesia Stock Exchange which are categorized as non-manipulator companies based on the calculation of the Beneish M-Score Model?
• How many bank subsector companies are listed on the Indonesia Stock Exchange which are categorized as gray companies based on the calculation of the Beneish M-Score Model?

2. Literature Review and Hypothesis Development

2.1. Literature Review

Regulatory theory consists of signaling, public interest, capture, and bushfire theories. Signaling theory, also called the theory of disclosure, states that an entity can increase its firm value through financial statements. Facing an increasingly competitive capital market with increasingly smart and sophisticated investors could trigger companies to maximize their value and provide all available information in their financial statements. Thus, these companies can be seen as better than companies that do not report their finances [26,27].

Signaling theory can be referred to as a self-regulating system or a self-perpetuating process. This is because each company has its reasons for issuing its financial statements. Signaling theory relies on the functioning of a perfect, free-market economy, in contrast to the fact that market economies are rarely perfect or free. Therefore, public interest theory states that the existence of regulations is a response to public demand to correct inefficiencies and injustices in market practice. Public interest theory is based on two assumptions: that the economic market is fragile, so it is likely to operate inefficiently and unfairly if left unregulated and that making regulations or rules comes almost without cost. In public interest theory, accounting standards-setting responds to an inefficient financial or accounting information market. Regulations are made in response to public demand for inefficient market conditions [28]. Therefore, as the name implies, the preparation of regulations with public interest theory are carried out based on the public’s wishes rather than the interests of the regulator [28]. The movement of the economy is a common issue and occurs worldwide. An economy will always move dynamically and can have short-term or long-term cycles. Cycles in the economy cannot be fully predicted or controlled, but the impact of these cycles can be minimized, for example, with countercyclical policies. A countercyclical policy can be defined as the implementation of pro-active government action to overcome extreme economic cyclical movements such as booms or recessions. Countercyclical policy uses approaches such as increasing taxes and reducing state budgets when the economy is booming or reducing tax collections and increasing spending when the economy is in recession.

The current economic phenomenon, namely the COVID-19 pandemic, can certainly impact the trend of slowing economic growth [6,22]. To overcome this, the Financial Services Authority (OJK) prepared various stimulus policies as countercyclical policies to anticipate the downturn risks that pandemic conditions could cause. Through Financial Services Authority Regulation (POJK) Number 11/POJK.03/2020 concerning the National Economic Stimulus as a countercyclical policy to alleviate the impact of the spread of COVID-19, the OJK prepared various measures, including the relaxation of asset quality determination, relaxation of credit restructuring arrangements, and provision of new funds for debtors affected by the spread of COVID-19. Despite facing great challenges, this countercyclical policy was expected to mitigate the impact of the weakening national economy [29]. The implementation of a credit restructuring policy can affect a company’s financial performance, which is an assessment of the achievement of a company [30]. Through the measurement of financial performance, researchers can determine a company’s overall health, which can form the basis for other analyses or comparisons. Financial performance is an evaluation of a company’s ability to overcome the risks of profitability, liquidity, and solvency and can be reviewed through components in the company’s financial statements in the form of their assets, liabilities, equity, revenue, costs, profits, and cash flow. An analysis carried out on the financial statements can provide benefits for both the internal and external parties of the company. Financial statements consist of a balance sheet (statement of financial position), a statement of profit or loss, a statement of changes in equity, and a statement of cash flow, which all reflect a company’s performance These statements can be used as a benchmark or basis for making investment decisions or accessing external credit and can be used as an evaluation tool for internal companies to improve their management [31]. For the analysis of financial performance to be relevant and useful for internal and external parties, the financial statements reported by the company should be free from fraud or manipulations [32].

The impact of the changes in the financial performance of banking companies due to implementing credit restructuring could trigger companies to manipulate or create fraudulent financial statements to appear more valuable than they are. According to [30], financial statement fraud is fraud committed by management in the form of material misstatements in financial statements that are detrimental to investors and creditors. Losses caused by this fraud can be financial or non-financial. The published financial reports, as one of the basic considerations for making investment decisions by investors to assess a company’s performance, should be free from fraud so that the integrity of the financial information is upheld and does not cause investors to make the wrong decisions.

According to SAS No. 99, fraudulent financial reporting can be carried out in various ways, including:

(a)

The manipulation, falsification, or alteration of the accounting records and supporting documents of the prepared financial statements.

(b)

Mistakes, omissions, or intentional omission of significant transactions, events, or information as a source of financial statement presentation.

(c)

The intentional abuse of the principles relating to amount, classification, presentation, or disclosure.

Fraudulent financial reporting includes several modes, such as [33]

(a)

Falsification, alteration, or manipulation of financial records, supporting documents, or business transactions

(b)

Intentional omission of significant events, transactions, accounts, or other information as a source of financial statement presentation.

(c)

Intentional omission of information that should be presented and disclosed regarding accounting principles and policies in preparing financial statements.

Research conducted by [30] revealed that several methods for detecting fraudulent financial statements are used. These methods are in the form of financial ratios according to fraud triangle theory which explains the three factors that cause fraud, which later developed into the fraud diamond theory, the fraud pentagon theory, and the fraud hexagon theory. The detection of fraudulent financial statements using financial ratios can be realized with the Beneish M-score model, which includes eight financial ratios. Professor Messod Beneish developed the Beneish M-score, a mathematical model to detect fraud in financial statements. Through a score whose calculation is based on eight ratios, this model can detect manipulations in earnings. The eight ratios are days’ sales in receivables index (DSRI), gross margin index (GMI), asset quality index (AQI), sales growth index (SGI), depreciation index (DEPI), sales general and administrative index (SGAI), leverage index (LVGI), and total accruals to total assets (TATA), which are based on the calculation year’s data (t) and the previous year’s data (t−1) taken from the company’s financial statements. In the results of research conducted by [34] on the 16 sample companies, 9 of them were classified as non-manipulators, 7 were classified as gray companies, and no company was classified as a manipulator. The similarity between the study in [34] and this study is the measurement variables, which include the 5 Beneish M-score model ratios in the form of days’ sales in receivables index, gross margin index, asset quality index, sales growth index, and total accruals to total assets. Meanwhile, one of the differences between the study in [34] and this study is the addition of the countercyclical policy factors in this study. In addition, other differences include the objects used for the research sample. The objects of research for the study in [34] were retail trading companies listed on the Indonesia Stock Exchange (IDX) in 2014, whereas the objects of this study were banking companies listed on the Indonesia Stock Exchange in 2018–2020.

The results of a study by [35] entitled Financial Statement Fraud Detection Using Beneish M-Score on JII and Non-JII Companies showed that the Beneish value of JII (Jakarta Islamic Index) companies was higher than that of non-JII companies. JII companies were shown to manipulate financial statements, whereas non-JII companies were shown to not manipulate financial statements [6]. The similarity of the study in [35] to this study is due to the same 5 research variables used: days’ sales in receivables index, gross margin index, asset quality index, sales growth index, and total accruals to total assets. Meanwhile, the differences between the study in [35] and this study are the 3 other Beneish M-score calculation variables used in that study (depreciation index, sales general, and administrative expenses index, and leverage index) as well as the addition of the countercyclical policy factors in this study. In addition, the study in [35] used samples of JII and non-JII companies listed on the IDX (2014–2016) as research objects in contrast to this study, which used banking companies listed on the IDX in 2018–2020 as research objects [36].

The study in [37] used 5 research variables: days’ sales in receivables index, gross margin index, asset quality index, sales growth index, and total accruals to total assets. The differences between that study and this study are the 3 other Beneish M-score calculation variables used in the study by [38] (depreciation index, sales general, and administrative expenses index, and leverage index) as well as the addition of the countercyclical policy factors in this study. In addition, the study in [38] used a sample of companies sanctioned by the OJK in the 2012–2017 period as the research objects in contrast to this study, which used banking companies listed on the Indonesia Stock Exchange from 2018–2020 as the research objects. The results showed that the Beneish M-score using financial ratios with data from before and after modifications was not effectively used. Of the eight ratios used, only AQI and TATA significantly affected the dummy Beneish M-score.

The study in [9] used 5 research variables: days’ sales in receivables index, gross margin index, asset quality index, sales growth index, and total accruals to total assets [37]. The differences between that study and this study are the 3 other Beneish M-score calculation variables used in the study by [39] (depreciation index, sales general, and administrative expenses index, and leverage index) as well as the addition of the countercyclical policy factors in this study. In addition, the study in [9] used a sample of property, real estate, and building construction companies that published annual financial reports for the 2014–2018 period as the research objects in contrast to this study, which used banking companies listed on the Indonesia Stock Exchange in 2018–2020 as the research objects. The results showed that the ratios of DSRI, SGI, DEPI, TATA, and LVGI had a significant effect on earnings management. Meanwhile, GMI, AQI, SGI, and SGAI did not affect earnings management [40].

The study in [7] used variables for calculating financial performance in the form of operating profit margin, current ratio, and interest multiples ratio. The difference between that study and this study is the addition of fraud detection variables using the Beneish M-score model in this study. In addition, the study in [7] used a sample of PT X as the object of research in contrast to this study, which used banking companies listed on the Indonesia Stock Exchange from 2018–2020 as the research objects. The results of the study in [7] indicated that the debt restructuring carried out by PT X was not able to improve financial performance.

The study in [17], focused on the credit restructuring factors. The difference between that study and this study is the use of financial performance ratio variables in the form of the capital adequacy ratio (CAR), earning assets quality (KAP), write-off, earning assets depreciation (PPAP), return on assets (ROA), operational expenditure on operating income (BOPO), loan to deposits ratio (LDR), and sash ratio in the study by [17], as well as the variables of operating profit margin, current ratio, and interest multiples ratio used in this study. In addition, this study has the addition of fraud detection variables using the Beneish M-score model. The objects of the two studies are also different, namely, rural banks (BPR) listed on the IDX as the objects of the study in [17] and banking companies listed on the IDX in 2018–2020 as the objects of this study. The results showed differences in the value of capital, assets, earnings, and banking soundness before and after the credit restructuring. However, there was no difference in the liquidity value before and after the credit restructuring.

2.2. Hypothesis Developments

The research proves that there was a significant difference in profitability and liquidity, but that there was no significant difference in the solvency of PT X before and after restructuring. Research conducted by [7] showed that the debt restructuring carried out by PT X was not able to improve financial performance. Meanwhile, research by [41] showed that there was no difference in the liquidity value before and after the credit restructuring. As such, the hypotheses for the analysis of the significance of the changes in financial performance can be formulated as follows:

Hypothesis 1 (H1).

There is a significant difference in a company’s profitability before and after the implementation of the restructuring.

Hypothesis 2 (H2).

There is a significant difference in a company’s liquidity before and after the implementation of the restructuring.

Hypothesis 3 (H3).

There is a significant difference in a company’s solvency before and after the implementation of the restructuring.

3. Research Methodology

3.1. Data and Sample Selection

The population in this study was the financial sector companies listed on the Indonesia Stock Exchange in 2020, with a total of 105 companies. The sample consisted of 32 banking companies in the commercial banking category that implemented countercyclical policies in the form of credit restructuring during the COVID-19 pandemic and were listed on the Indonesia Stock Exchange for the 2018–2020 period. The sample selection in this study used the non-probability sampling technique with the purposive sampling selection method, namely, the determination of the sample based on the criteria determined by the researcher.

The data on the financial statements of the banking sub-sector companies that carried out credit restructuring, which were used in this study, are secondary data accessed through the official website of the Indonesia Stock Exchange (www.IDX.co.id, accessed on 30 November 2021) and the official website of each company. The type of data used in this study are panel data, namely, data that combine two other types of data, cross-section data and time-series data. In this case, the panel data used include the financial statements of 32 companies as the objects (cross-section data) from the 3 years from 2018 to 2020 (time-series data). Using quantitative methods, this study employed a ratio measurement scale with 3 ratios to measure financial performance and 5 ratios to measure the Beneish M-score model. The research sample selection process is presented in

Table 1. Research Sample Selection Process.

No.	Criteria	Amount
1	Financial sector companies listed on the Indonesia Stock Exchange during 2018–2020	105
2	Financial sector companies that were not in the category of commercial banks listed on the Indonesia Stock Exchange in 2018–2020	58
3	Commercial banks that did not implement countercyclical policies in the form of credit restructuring	15
	Sample companies that met the criteria	32
	Total research sample = 32 companies × 3 years	96

Sources: data processing results.

below:

The selection criteria for the sample of banking sub-sector companies can be seen in Table 1; specifically, commercial banks that carried out restructuring were used to fulfill the research objective, namely, to observe the significance of the changes in financial performance that occurred due to the implementation of the countercyclical policy as this policy was applied to banking sub-sector companies in Indonesia during the COVID-19 pandemic. Thus, banks as the institutions most affected by countercyclical policies, can be a relevant sample for further reviews regarding the potential for the manipulation of financial statements.

3.2. Data Analysis Method

Analysis of the significance of the changes in the financial performance of banking companies before and after the implementation of countercyclical policies was carried out using a quantitative approach method, namely, research by collecting numerical data, which was then further processed using statistical formulas or techniques with measuring scales, such as nominal, ordinal, and interval, and ratios [41]. This study used a quantitative approach to present facts related to the changes in financial performance and to test a theory (Beneish M-score model).

The testing of the significance of the changes in financial performance was carried out using a comparative descriptive method. The comparative descriptive method provided an overview in the form of a description of the countercyclical policy phenomenon that occurred. This method can demonstrate the impact based on a comparison of time in a study. According to [42], the descriptive method is conducted by examining a particular object. Descriptive research describes the facts on the phenomenon under study. Furthermore, comparative research, according to [43], is a study that compares a variable or more at different samples or times. In this study, comparisons were made based on different times, namely, before and after the implementation of the countercyclical policy.

The analysis for comparing the position of the companies’ financial statements before and after the implementation of the countercyclical policies to determine the significance of the changes in the companies’ financial performance was measured using 3 ratios:

⬤

Profitability Ratio

The profitability ratio is proxied by the ratio of the operating profit margin. The ratio calculation, done by dividing the operating profit by net sales, aims to show stakeholders the percentage of returns a company receives after paying for all of its operational costs.

⬤

Liquidity Ratio

The liquidity ratio is proxied by the current ratio. By dividing current assets by current liabilities, the current ratio can measure a company’s ability to pay off its short-term obligations and show stakeholders how it can manage and maximize current assets to meet all of its short-term obligations.

⬤

Solvency Ratio

The solvency ratio is proxied by the interest multiples ratio. By dividing pre-tax income by interest expenses, the interest multiples ratio can measure a company’s ability to generate pre-tax income to pay interest costs.

The data analysis method was carried out by calculating the companies’ financial performance based on the profitability, liquidity, and solvency ratios of the financial statement data before the restructuring, namely the 2018 and 2019 reporting periods, as well as after the restructuring policy was carried out in 2020. The formula for the profitability, liquidity, and solvency ratios can be seen in

Table 2. Formula for profitability, liquidity, and solvency ratios.

No	Variable	Measurement Indicator
1.	Operating profit margin	$OPM=\frac{Operational Profit}{Net Sales}$
2.	Current ratio	$CR=\frac{Current Assets}{Current Liability}$
3.	Time Interest earned ratio	$TIER=\frac{Earnings Before Interest and Expense \left(EBIT\right)}{Interest Expense}$

Sources: [38]

.

After grouping the data based on the reporting periods before and after the implementation of the restructuring policy, descriptive statistical analysis was carried out using normality tests and hypothesis testing.

• Normality Test

This test was carried out to ensure that the data were normally distributed. The results of the normality test determined the hypothesis test to be used.

Research using this type of panel data has several advantages that are described by [44], one of which is that panel data can take into account and explicitly control individual heterogeneity, meaning that panel data can be used as a sample for more complex behavioral models. In addition, panel data based on repeated cross-sectional observations (time series) give the number of panel data observations a higher degree of freedom and are more efficient in generating estimates. The advantages possessed by the characteristics of the panel data mean that the classical assumption test was not carried out in the panel data model [45]. In their research, Ref. [44] explained that large samples or data with more than 30 numbers (n > 30) could be assumed to be normally distributed. Nevertheless, statistical normality tests can still be carried out to provide certainty regarding the normality of the data distribution and as a reference for determining the hypothesis test to be used. This is also explained in [19], which stated that large samples can ignore the normality test, considering panel data characteristics that combine cross-sectional data with time-series data.

2.

Hypothesis Test

The results of the normality test determined the test equipment to be used in testing the hypotheses. Normally distributed data can use the independent sample t-test, which is a parametric test, to test the hypotheses. In contrast, data that are not normally distributed are tested using the non-parametric Mann–Whitney U Test.

The hypotheses in this analysis include:

Hypothesis 0a (H0a).

There is no significant difference in a company’s profitability before and after the implementation of the restructuring.

Hypothesis 0b (H0b).

There is no significant difference in a company’s liquidity before and after the implementation of the restructuring.

Hypothesis 0c (H0c).

There is no significant difference in a company’s solvency before and after the implementation of the restructuring.

The decision-making criteria for the independent sample t-test and Mann–Whitney U Test with a probability value of 0.025 (2-tailed) are as follows:

• If the probability (Asymp. Sig) < 0.025, H0 is rejected, and H1 is accepted.
• If the probability (Asymp. Sig) > 0.025, H0 is accepted, and H1 is rejected.

Then, to determine the categories of manipulator, non-manipulator, and gray companies, research was conducted using the financial ratios of the Beneish M-score model. This study used an index ratio analysis technique whose calculation results were used as a reference for determining the company categories based on the Beneish M-score model index parameters.

The following were the steps for data analysis using the Beneish M-score model:

• Perform Beneish M-score index calculations

  (a)

  Days’ Sales in Receivables Index (DSRI)

  • Calculation of Beneish M-score index

    (a)

    Days’ Sales in Receivables Index (DSRI)

    $$DSRI=\frac{Accounts receivabl{e}_{\left(t\right)}:Sal{e}_{\left(t\right)}}{Accounts receivabl{e}_{\left(t-1\right)}:Sal{e}_{\left(t-1\right)}}$$

    (b)

    Gross Margin Index (GMI)

    $$GMI=\frac{Gross profi{t}_{\left(t-1\right)}:Sale{s}_{\left(t-1\right)}}{Gross profi{t}_{\left(t\right)}:Sale{s}_{\left(t\right)}}$$

    (c)

    Assets Quality Index (AQI)

    $$AQI=\frac{1-\frac{Current asse{t}_{\left(t\right)}+fixed asset{s}_{\left(t\right)}}{Total Asset{s}_{\left(t\right)}}}{1-\frac{Current asse{t}_{\left(t-1\right)}+Fixed asset{s}_{\left(t-1\right)}}{Total Asset{s}_{\left(t-1\right)}}}$$

    (d)

    Sales Growth Index (SGI)

    $$SGI=\frac{Sale{s}_{\left(t\right)}}{Sale{s}_{\left(t-1\right)}}$$

    (e)

    Total Accrual to Total Asset (TATA)

    $$TATA=\frac{Operating profi{t}_{\left(t\right)}-Cash flow from Operating Activitie{s}_{\left(t\right)}}{Total Asset{s}_{\left(t\right)}}$$

    Information:

    t = period t

    t – 1 = period t − 1

  • Compare the calculated indexes and the Beneish M-score model parameter indexes

    (a)

    Days’ Sales in Receivables Index (DSRI)

    Table 3 describes the parameter index days’ sales in receivables

    Table 3. Days’ Sales in Receivables Parameter Index.

    No.	Index	Information
    1	≤1.031	Non-Manipulator
    2	1.031 < index < 1.465	Grey Company
    3	≥ 1.465	Manipulator

    Sources: [16]

    Table 3. Days’ Sales in Receivables Parameter Index.

    No.	Index	Information
    1	≤1.031	Non-Manipulator
    2	1.031 < index < 1.465	Grey Company
    3	≥ 1.465	Manipulator

    Sources: [16]

    (b)

    Gross Margin Index (GMI)

    Table 4 describes the gross margin parameter index

    Table 4. Gross Margin Parameter Index.

    No.	Index	Information
    1	≤1.014	Non-Manipulator
    2	1.014 < index < 1.193	Grey Company
    3	≥1.193	Manipulator

    Sources: [16]

    Table 4. Gross Margin Parameter Index.

    No.	Index	Information
    1	≤1.014	Non-Manipulator
    2	1.014 < index < 1.193	Grey Company
    3	≥1.193	Manipulator

    Sources: [16]

    (c)

    Assets Quality Index (AQI)

    Table 5 describes the assets quality parameter index

    Table 5. Assets Quality Parameter Index.

    No.	Index	Information
    1	≤1.039	Non-Manipulator
    2	1.039 < index < 1.254	Grey Company
    3	≥1.254	Manipulator

    Sources: [16]

    Table 5. Assets Quality Parameter Index.

    No.	Index	Information
    1	≤1.039	Non-Manipulator
    2	1.039 < index < 1.254	Grey Company
    3	≥1.254	Manipulator

    Sources: [16]

    (d)

    Sales Growth Index (SGI)

    Table 6 describes the sales growth parameter index

    Table 6. Sales Growth Parameter Index.

    No.	Index	Information
    1	≤1.134	Non-Manipulator
    2	1.134 < index < 1.607	Grey Company
    3	≥1.607	Manipulator

    Source: [16]

    Table 6. Sales Growth Parameter Index.

    No.	Index	Information
    1	≤1.134	Non-Manipulator
    2	1.134 < index < 1.607	Grey Company
    3	≥1.607	Manipulator

    Source: [16]

    (e)

    Total Accrual to Total Assets (TATA)

    Table 7 describes the total accrual to total assets

    Table 7. Total Accrual to Total Assets Parameter Index.

    No.	Index	Information
    1	≤0.018	Non-Manipulator
    2	0.018 < index < 0.031	Grey Company
    3	≥0.031	Manipulator

    Sources: [16]

    Table 7. Total Accrual to Total Assets Parameter Index.

    No.	Index	Information
    1	≤0.018	Non-Manipulator
    2	0.018 < index < 0.031	Grey Company
    3	≥0.031	Manipulator

    Sources: [16]

  • Determine the company category
    Classify manipulator, non-manipulator, and gray companies based on the following criteria [46]:

    (a)

    Companies with 3 parameter indices indicating manipulator were classified as manipulator companies.

    (b)

    Companies with 3 parameter indices indicating non-manipulator were classified as non-manipulator companies.

    (c)

    Companies with 3 parameter indices indicating gray company that do not have 2 other parameter indices indicating manipulator or non-manipulator were classified as gray companies.

Furthermore, the classification of companies according to the criteria of manipulator, non-manipulator, and gray companies was measured using the five ratios of the Beneish M-score model [16,47].

⬤

Days’ Sales in Receivables Index (DSRI)

As an index used to measure the number of days of credit sales in the calculation year (year t) against the previous year (year t − 1), DSRI can act as a calculation metric showing an increase or decrease in the quality of income. If the DSRI is more than 1, it indicates that there is a potential for manipulation in the form of an overstatement of earnings. This is because the higher number of days could indicate a change in credit policy, which could trigger an increase in sales. In addition, the value of the receivables that experience an increase that is not in line with the increase in sales could indicate a manipulation of profit or income.

⬤

Gross Margin Index (GMI)

The gross margin index, measured by comparing the changes in gross profit for the calculation year to those of the previous year, is one of the ways used to assess an increase or decrease in company profitability, which can be seen from its earnings management. A GMI of more than 1 could indicate manipulation in the form of an overstatement in earnings.

⬤

Assets Quality Index (AQI)

The assets quality index compares non-current assets other than fixed assets (property, plant, and equipment) and total assets in the calculation year with those of the previous year. This index measures the quality of a company’s assets, especially the non-current assets, whose benefits can be gained in the future. An index value of more than 1 indicates an increase in non-current assets, resulting in a decrease in asset quality and the number of deferred expenses. This can indicate manipulation in the form of an overstatement in earnings.

⬤

Sales Growth Index (SGI)

The sales growth index is a ratio that measures sales in the research year (year t) to those of the previous year (year t − 1). Thus, an index value of more than 1 means that a company’s sales in the study year increased from those of the previous year and could indicate manipulation in the form of an overstatement of earnings.

⬤

Total Accrual to Total Asset (TATA)

Calculating total accruals as changes in working capital accounts other than cash (cash) and depreciation on total assets can produce estimates of a company’s income and expenditure activities. In this case, there is a potential for fraud or profit manipulation to be carried out on accruals, especially if the accrual value is too high. High total accruals indicate that the cash portion of the profits generated is low. Thus, a high TATA value can indicate manipulation in the form of an overstatement of profit.

3.3. Analytical Methods

Testing the significance of the changes in financial performance was carried out using a comparative descriptive method. According to [42], the descriptive method is carried out by examining a particular object [27]. Descriptive research describes the facts of the phenomenon under study. Furthermore, comparative research according to [43] is a study that compares one or more variables at different samples or times. In this study, comparisons were made based on different times, namely, before and after implementing the countercyclical policy. The comparative descriptive method provided an overview in the form of a description of the countercyclical policy phenomenon that occurred. It showed an impact based on a comparison of the different periods used in the study. The data analysis method was carried out by calculating the companies’ financial performance based on the profitability, liquidity, and solvency ratios of the company’s financial statement data before restructuring, namely, the 2018 and 2019 reporting periods, as well as after the restructuring policy was carried out in 2020. After classifying data based on the reporting times before and after the implementation of the restructuring policy, descriptive statistical analysis was carried out with the normality test and hypothesis testing using the Mann–Whitney U Test. Then, to determine the categories of manipulator, non-manipulator, and gray companies, calculations were conducted using the financial ratios of the Beneish M-score model. This study used an index ratio analysis technique, whose calculation results were used as a reference for determining the company categories based on the Beneish M-score model index parameters. The steps of the data analysis using the Beneish M-score model included calculating the Beneish M-score index, comparing the arithmetic index to the Beneish M-score model parameter index, and determining the categories of companies based on predetermined criteria. Regulatory theory consists of signaling, public interest, capture, and bushfire theories. Signaling theory, also called the theory of disclosure, states that an entity can increase its firm value through financial statements. Facing an increasingly competitive capital market with increasingly smart and sophisticated investors could trigger a company to maximize its value and provide all available information in their financial statements. Thus, these companies can be seen as better than companies that do not report their finances.

4. Results

Data Analysis of the Impact of Countercyclical Policies on Company’s Financial Performance

4.1. Statistical Descriptive Analysis

Descriptive statistical analysis was conducted to determine the minimum, maximum, average, and standard deviation of the operating profit margin, current, and interest multiples ratios of the 32 sample companies. The calculation of the financial performance ratios was carried out in the period before the implementation of the countercyclical policy (in 2018 and 2019) and after the implementation of the countercyclical policy (in 2020) and can be seen in

Table 8. Statistical Descriptive Results.

	N	Minimum	Maximum	Mean	Std. Deviation
Current Ratio	96	0.6303	2.2206	1.149753	0.2176437
Operating Profit Margin	96	−1.6123	0.4389	0.050282	0.3028143
Interest Multiples Ratio	96	−6.4553	3.9861	1.245791	1.0598731
Valid N (listwise)	96

Sources: data processing results.

below.

4.2. Classic Assumption Test (Normality Test)

The following are the results of the normality test of the current, operating profit margin, and interest multiples ratios before and after the implementation of the countercyclical policy. The normality test results can be seen in

Table 9. Normality Test Results.

	Countercyclical	Kolmogorov–Smirnov a			Shapiro–Wilk
		Statistic	df	Sig.	Statistic	df	Sig.
Current Ratio	Before	0.157	64	0.000	0.895	64	0.000
	After	0.205	32	0.001	0.825	32	0.000
Operating Profit Margin	Before	0.261	64	0.000	0.631	64	0.000
	After	0.344	32	0.000	0.669	32	0.000
Interest Multiples Ratio	Before	0.205	64	0.000	0.790	64	0.000
	After	0.304	32	0.000	0.544	32	0.000

^a. Lilliefors Significance Correction. Sources: data processing results.

below.

Normality test assessment criteria:

• • If the value of Sig. > 0.05, then the assumption of the normality of the data is met or the data is normally distributed.
• • If the value of Sig. < 0.05, then the assumption of the normality of the data is not met or the data is not normally distributed.

Normality test results:

Based on Kolmogorov–Smirnov and Shapiro–Wilk processing results, the significance value of the current ratio, operating profit margin, and time interest Earned Ratio before and after the implementation of the countercyclical policies was less than 0.05. Thus, it can be concluded that the data is not normally distributed, so the normality assumption is not met.

Hypothesis Testing

Hypothesis testing or tests on data that are generally not distributed was carried out using the Mann–Whitney U Test. The results of the Mann–Whitney U test on the data of the current ratio, operating profit margin ratio, and interest multiples ratio before and after the implementation of the countercyclical policies can be seen in

Table 10. Mann–Whitney U Test Results.

	Current Ratio	Operating Profit Margin	Interest Multiples Ratio
Mann–Whitney U	874.000	921.000	923.000
Wilcoxon W	1402.000	1449.000	1451.000
Z	−1.166	−0.801	−0.785
Asymp. Sig. (2-tailed)	0.244	0.423	0.432

Grouping Variable: Countercyclical. Sources: data processing results.

below.

Decision-making criteria in the Mann–Whitney U Test:

• If the probability (Asymp. Sig.) < 0.025, H0 is rejected and H1 is accepted
• If the probability (Asymp. Sig.) > 0.025, H0 is accepted and H1 is rejected.

Mann–Whitney U Test Results

Based on the processing results, an Asymp. Sig. value of 0.244 was obtained for the current ratio, an Asymp. Sig. value of 0.465 was obtained for the operating profit margin, and an Asymp. Sig. value of 0.499 was obtained for the time interest earned ratio. The acquisition value of Asymp. Sig. for the three ratios of more than 0.025 means no significant difference in the companies’ profitability, liquidity, and solvency before and after the implementation of the restructuring.

Data Analysis of Fraudulent Financial Statement Detection Using Beneish M-Score Model

4.3. Beneish M-Score Index Calculation

The following are the results of the calculations of the 5 index ratios for the 32 companies in 2018–2020. The index ratio calculation results for 2018 can be seen in

Table 11. Index Ratio Calculation Results for 2018.

No.	Company	DSRI	GMI	AQI	SGI	TATA
1	AGRO	1.10532	1.02375	0.00002	1.29575	−0.10303
2	AGRS	1.01205	0.78889	0.54762	1.07044	−0.04544
3	AMAR	1.19628	1.10633	0.22911	3.32476	0.01134
4	ARTO	1.01401	1.06189	−0.00662	0.79434	0.15701
5	BABP	1.01545	0.78559	1.68465	1.04466	0.00897
6	BACA	0.99829	0.82398	7.97692	1.11989	−0.08249
7	BBCA	1.02080	0.96981	5.76996	1.16961	0.03370
8	BBKP	1.07487	0.95102	1.96798	0.91261	0.06590
9	BBMD	1.09622	1.05083	20.83246	0.99055	0.04749
10	BBNI	0.95403	0.96264	1.03235	1.29548	0.02953
11	BBRI	1.02582	1.00774	0.34234	1.10232	−0.00915
12	BBTN	0.93646	1.01325	5.47579	1.26696	0.01662
13	BCIC	0.97990	1.21831	6.86955	0.91027	−0.08640
14	BGTG	1.00844	0.98228	1.50656	0.99512	0.00996
15	BINA	0.99538	0.99845	16.14356	1.12441	−0.12685
16	BKSW	0.98679	1.04811	4.27591	0.73477	0.14261
17	BMRI	0.92959	0.94936	1.11128	1.35281	0.04468
18	BNGA	0.81310	0.90028	1.13496	1.28154	0.02078
19	BNII	1.02193	0.94314	29.76129	1.18875	0.05841
20	BNLI	1.15934	1.03451	1.39829	0.90758	0.03814
21	BSIM	1.67869	1.00161	4.43536	0.64227	0.03337
22	BSWD	1.03618	0.88508	7.82002	1.08040	0.21184
23	BTPN	0.88975	0.92260	0.86093	1.27387	−0.01616
24	BVIC	1.14625	1.04546	2.55080	1.02791	0.04437
25	DNAR	1.04192	0.65700	0.95715	2.11593	−0.00018
26	INPC	0.94586	0.93915	1.21160	0.94081	−0.04161
27	MAYA	1.05862	1.00174	0.11992	1.14490	0.04484
28	MEGA	1.12740	1.05200	14.27938	1.02848	0.06152
29	NISP	1.02570	1.06664	1.22183	1.08634	−0.02784
30	NOBU	0.88584	0.94179	1.30784	1.26620	−0.01415
31	PNBN	1.02335	0.90375	0.94713	1.18808	0.07477
32	SDRA	1.07004	0.95051	0.91245	1.10832	0.16008

Sources: data processing results.

below.

The index ratio calculation results for 2019 can be seen in

Table 12. Index Ratio Calculation Results for 2019.

No.	Company	DSRI	GMI	AQI	SGI	TATA
1	AGRO	0.99103	1.17481	1.67506	1.24229	0.09954
2	AGRS	0.95292	1.33995	−1.51702	1.46259	0.06044
3	AMAR	0.68855	0.98136	0.24061	2.18756	−0.15357
4	ARTO	0.89467	1.51823	0.04323	0.81029	0.45040
5	BABP	0.93072	1.08305	2.19048	1.07525	0.08066
6	BACA	1.04809	1.17124	1.22227	1.14804	0.11615
7	BBCA	0.95422	1.00378	1.16271	1.14054	−0.01703
8	BBKP	1.07620	1.18071	1.31054	0.97215	0.02233
9	BBMD	1.03456	1.01016	1.16408	1.03158	0.00107
10	BBNI	0.98562	1.04934	0.85975	1.08245	0.03796
11	BBRI	0.97181	1.02119	6.35475	1.10829	−0.00522
12	BBTN	0.93432	1.13713	0.14363	1.16305	0.04923
13	BCIC	0.57422	0.99596	0.11086	1.04718	0.00664
14	BGTG	1.22723	1.14633	1.14175	1.02961	−0.06487
15	BINA	1.20214	1.14981	0.15275	1.29420	−0.07699
16	BKSW	1.23819	0.88855	0.87786	0.99656	0.03209
17	BMRI	1.01465	1.03522	1.03755	1.08597	0.01859
18	BNGA	0.95288	1.01324	0.85152	1.07450	0.01043
19	BNII	0.87983	1.02039	0.98769	1.06008	−0.02083
20	BNLI	0.94722	0.99734	1.40150	1.06365	0.02410
21	BSIM	0.79427	0.96725	1.47476	1.44750	0.03540
22	BSWD	0.80862	0.81043	1.50778	1.05030	−0.10480
23	BTPN	1.50156	1.11508	0.66960	1.37814	0.08811
24	BVIC	1.08095	1.27785	1.03887	0.97509	0.03702
25	DNAR	1.38020	1.12818	0.89091	0.95691	0.03747
26	INPC	1.01243	1.05660	1.98075	0.92615	−0.02589
27	MAYA	1.01747	1.13407	13.20595	1.11788	0.04297
28	MEGA	1.15174	1.04342	0.77965	1.10850	−0.01164
29	NISP	0.89250	1.05399	1.02927	1.13049	−0.00406
30	NOBU	1.37380	1.07326	1.75633	1.09918	0.16457
31	PNBN	0.99152	1.04600	1.22095	0.99995	−0.00107
32	SDRA	1.06875	1.20136	0.78976	1.11884	0.02721

below.

The index ratio calculation results for 2022 can be seen in

Table 13. Index Ratio Calculation Results for 2020.

No.	Company	DSRI	GMI	AQI	SGI	TATA
1	AGRO	1.06895	1.03313	1.10666	0.94271	−0.02518
2	AGRS	1.46911	0.76651	−11.91448	0.83612	0.22841
3	AMAR	0.78971	1.05459	2.03240	1.05887	−0.17655
4	ARTO	1.60417	0.37315	33.65170	1.98739	0.23529
5	BABP	1.03282	0.98542	0.99722	0.91329	−0.07789
6	BACA	0.81771	1.39268	11.27718	0.83127	0.00962
7	BBCA	0.95700	0.96884	1.13454	1.01676	−0.01619
8	BBKP	1.20230	1.37624	0.38883	0.72884	0.11915
9	BBMD	0.82821	0.92978	1.65220	1.09938	−0.10500
10	BBNI	1.11722	0.96546	0.66965	0.95540	−0.07744
11	BBRI	1.08486	0.99002	4.08353	0.96674	−0.02717
12	BBTN	1.14526	1.10174	−0.72440	0.89720	−0.07898
13	BCIC	1.80453	4.16087	1.85070	0.64259	0.09227
14	BGTG	1.18412	0.96961	0.71830	0.91876	−0.18340
15	BINA	0.97591	0.94742	−4.91091	1.34730	−0.26557
16	BKSW	1.06026	1.39998	0.57387	0.84416	0.21868
17	BMRI	1.04820	0.98946	1.13932	0.99261	−0.05338
18	BNGA	0.95331	0.97334	2.29743	0.95064	−0.09173
19	BNII	1.09597	0.93967	1.45223	0.84094	−0.16551
20	BNLI	1.27690	0.91319	0.83215	1.02278	0.00236
21	BSIM	1.15109	1.00439	1.29107	0.91859	−0.04483
22	BSWD	1.39577	1.43644	1.08118	0.68057	0.03387
23	BTPN	1.07741	0.91259	−0.28935	0.89166	−0.08753
24	BVIC	0.93903	0.82685	1.32909	0.87990	−0.01078
25	DNAR	1.03181	0.88837	0.77805	1.13571	0.09584
26	INPC	1.53596	1.16315	1.07721	0.87687	−0.16638
27	MAYA	1.22435	7.28342	47.56125	0.58415	−0.00446
28	MEGA	1.05180	0.97051	1.20191	1.12008	0.03815
29	NISP	1.21413	0.87991	1.18124	0.95627	−0.01213
30	NOBU	1.16376	0.97625	2.23593	0.96831	0.06636
31	PNBN	0.91231	0.89487	0.12396	0.98954	−0.11005
32	SDRA	1.16775	0.93025	0.94463	0.96773	0.14673

below.

4.4. Calculation of Company Categories

Manipulator, non-manipulator, and gray companies were classified based on the following criteria [19,28] (Darmawan, 2016):

(a)

Companies with three parameter indices indicating manipulator are classified as manipulator companies.

(b)

Companies with three parameter indices indicating non-manipulator are classified as non-manipulator companies.

(c)

Companies with three parameter indices indicating gray company that do not have two other parameter indices indicating manipulator or non-manipulator are classified as gray companies.

Referring to the calculations of the days’ sales in receivables index, gross margin index, assets quality index, sales growth index, and total accrual to total assets ratios of the 32 banking sub-sector companies in 2018–2020 along with the parameter indexes as a comparison for determining the categories, the following are the categories of each company in each reporting year. The company categories for 2018 can be seen in

Table 14. Company Categories for 2018.

No.	Company	DSRI	GMI	AQI	SGI	TATA	Result
1	AGRO	G	G	N	G	N	G
2	AGRS	N	N	N	N	N	N
3	AMAR	G	G	N	M	N	G
4	ARTO	N	G	N	N	M	N
5	BABP	N	N	M	N	N	N
6	BACA	N	N	M	N	N	N
7	BBCA	N	N	M	G	M	G
8	BBKP	G	N	M	N	M	G
9	BBMD	G	G	M	N	M	G
10	BBNI	N	N	N	G	G	N
11	BBRI	N	N	N	N	N	N
12	BBTN	N	N	M	G	N	N
13	BCIC	N	M	M	N	N	N
14	BGTG	N	N	M	N	N	N
15	BINA	N	N	N	N	N	N
16	BKSW	N	G	M	N	M	G
17	BMRI	N	N	G	G	M	G
18	BNGA	N	N	G	G	G	G
19	BNII	N	N	M	G	M	G
20	BNLI	G	G	M	N	M	G
21	BSIM	M	N	M	N	M	M
22	BSWD	G	N	M	N	M	G
23	BTPN	N	N	N	G	N	N
24	BVIC	G	G	M	N	M	G
25	DNAR	G	N	N	M	N	N
26	INPC	N	N	G	N	N	N
27	MAYA	G	N	N	G	M	G
28	MEGA	G	G	M	N	M	G
29	NISP	N	G	G	N	N	N
30	NOBU	N	N	M	G	N	N
31	PNBN	N	N	N	G	M	N
32	SDRA	G	N	N	N	M	N

Sources: data processing result.

below.

The company categories for 2019 can be seen in

Table 15. Company Categories for 2019.

No.	Company	DSRI	GMI	AQI	SGI	TATA	Result
1	AGRO	N	G	M	G	M	G
2	AGRS	N	M	N	G	M	G
3	AMAR	N	N	N	M	N	N
4	ARTO	N	M	N	N	M	N
5	BABP	N	G	M	N	M	G
6	BACA	G	G	G	G	M	G
7	BBCA	N	N	G	G	N	N
8	BBKP	G	G	M	N	G	G
9	BBMD	G	N	G	N	N	N
10	BBNI	N	G	N	N	M	N
11	BBRI	N	G	M	N	N	N
12	BBTN	N	G	N	G	M	G
13	BCIC	N	N	N	N	N	N
14	BGTG	G	G	G	N	N	G
15	BINA	G	G	N	G	N	G
16	BKSW	G	N	N	N	M	N
17	BMRI	N	G	N	N	G	N
18	BNGA	N	N	N	N	N	N
19	BNII	N	G	N	N	N	N
20	BNLI	N	N	M	N	G	N
21	BSIM	N	N	M	G	M	G
22	BSWD	N	N	M	N	N	N
23	BTPN	M	G	N	G	M	G
24	BVIC	G	M	N	N	M	G
25	DNAR	G	G	N	N	M	G
26	INPC	N	G	M	N	N	N
27	MAYA	N	G	M	N	M	G
28	MEGA	G	G	N	N	N	N
29	NISP	N	G	N	N	N	N
30	NOBU	G	G	M	N	M	G
31	PNBN	N	G	G	N	N	N
32	SDRA	G	M	N	N	G	G

Sources: data processing results.

below.

The company categories for 2020 can be seen in

Table 16. Company Categories for 2020.

No.	Company	DSRI	GMI	AQI	SGI	TATA	Result
1	AGRO	G	G	G	N	N	G
2	AGRS	M	N	N	N	M	N
3	AMAR	N	G	M	N	N	N
4	ARTO	M	N	M	M	M	M
5	BABP	G	N	N	N	N	N
6	BACA	N	M	M	N	N	N
7	BBCA	N	N	G	N	N	N
8	BBKP	G	M	N	N	M	G
9	BBMD	N	N	M	N	N	N
10	BBNI	G	N	N	N	N	N
11	BBRI	G	N	M	N	N	N
12	BBTN	G	G	N	N	N	N
13	BCIC	M	M	M	N	M	M
14	BGTG	G	N	N	N	N	N
15	BINA	N	N	N	G	N	N
16	BKSW	G	M	N	N	M	G
17	BMRI	G	N	G	N	N	N
18	BNGA	N	N	M	N	N	N
19	BNII	G	N	M	N	N	N
20	BNLI	G	N	N	N	N	N
21	BSIM	G	N	M	N	N	N
22	BSWD	G	M	G	N	M	G
23	BTPN	G	N	N	N	N	N
24	BVIC	N	N	M	N	N	N
25	DNAR	G	N	N	G	M	G
26	INPC	M	G	G	N	N	G
27	MAYA	G	M	M	N	N	G
28	MEGA	G	N	G	N	M	G
29	NISP	G	N	G	N	N	N
30	NOBU	G	N	M	N	M	G
31	PNBN	N	N	N	N	N	N
32	SDRA	G	N	N	N	M	N

below.

The summary of the company categories can be seen in

Table 17. Company Category Summary.

Category	2018	2019	2020
Non-Manipulator	17	17	21
Grey Company	14	15	9
Manipulator	1	0	2
Number of companies	32	32	32

Sources: data processing results.

below.

The company category percentages can be seen in

Table 18. Company Category Percentages.

Category	2018	2019	2020
Non-Manipulator	17	17	21
Grey Company	14	15	9
Manipulator	1	0	2
Number of companies	32	32	32

Sources: data processing results.

below.

The detection of financial statement fraud using the Beneish M-score model on the financial statement data of 32 banking sub-sector companies in 2018–2020 that was carried out following credit restructuring showed that some companies potentially manipulated or created fraudulent financial statements. Based on Table 18, there was one company classified as a manipulator in 2018 and two companies in 2020. However, more than half the companies in the research sample were classified as non-manipulators every year. This shows that these companies have been responsible in presenting reliable financial statements or not doing any manipulations.

5. Discussions

5.1. Company Financial Performance

• Current Ratio

The current ratio of the 32 sample companies in 2018 averaged 1.14585. Twenty-nine had a current ratio of more than 1, whereas the other three companies had a current ratio of less than 1. In 2019, the average current ratio of all the sample companies was 1.17514, with 5 companies having a current ratio of less than 1 and 27 companies having a current ratio of more than 1. Looking at the current ratio results, companies with a current ratio of less than 1 are stable and tend to be very close to 1 (0.86–0.99); this indicates that there is no significant liquidity problem [22]. Thus, the banking sub-sector companies in the period before the implementation of the countercyclical policy tended to be able to pay their current liabilities with their current assets. In 2020 or after the countercyclical policy was implemented, the average value of the current ratio of the sample companies was 1.12827, with a total of 23 companies recording a current ratio of more than 1. Despite the decline in the current ratio in several entities, the average overall current ratio did not experience a significant decrease from 2019, which was only 0.04688.

b.

Operating Profit Margin Ratio

The operating profit margin ratio before the countercyclical policy was implemented varied, with an average of 11.13% in 2018 and 4.92% in 2019. There was a decline; the average operating profit margin ratio in 2020 after the implementation of the countercyclical policy restructuring only amounted to −0.97%.

In this case, PT Bank Jago Tbk recorded the lowest operating profit margin percentage in 2019 and 2020 of less than −150%, which was a significant decrease from the −25.64% in 2018. This is because PT Bank Jago Tbk made a digital investment in a mobile banking application, thereby increasing expenditure and reducing the company’s operating profit composition. On the other hand, PT Bank Central Asia Tbk is the largest private bank in Indonesia with a market capitalization value of above IDR 100 trillion and it recorded the highest operating profit margin ratio from 2018 to 2020 of 38–43%. According to the President Director of PT Bank Central Asia Tbk, Jahja Setiaatmadja, credit restructuring for customers can help bank lending run optimally to increase bank profitability.

Regarding the average operating profit margin ratio of all the research samples, which declined from year to year, it can be concluded that there was a decline in the profitability of the banking sub-sector companies not only after the implementation of countercyclical policies but also in 2019. In the decline in bank profitability that occurred in 2019, almost all companies involved in the banking industry felt the impact of the lack of credit distribution. This caused a decrease in the companies’ net profits. A report from [48,49] stated that the Financial Services Authority (OJK) recorded a banking profit growth in November 2019 of 6.9%, which was lower than the profit growth in 2018 that reached 14.3%. Again, after experiencing a decline in 2020, the profit performance of the banking industry tended to be affected by the decline in the national economy due to the COVID-19 pandemic. The Minister of SOEs, Erick Thohir, said that the decline in performance was partly due to the active role of state-owned banks in restructuring MSMEs, among others. Banks that are members of the State Bank Association (Himbara), as well as private banks, were severely affected by the COVID-19 pandemic and the restructuring policies implemented.

c.

Multiply Interest Earned Ratio (Time Interest Earned Ratio)

Overall, the ratios of the multiples of interest generated by the entire research sample were quite good. In 2018, 29 companies recorded an attractive multiple of more than 1, and 3 other companies recorded an attractive multiple of less than 1. Then in 2019, 27 companies recorded an attractive multiple of more than 1, and 5 other companies recorded an attractive multiple of less than 1 including PT Bank Jago Tbk as the only company that recorded a negative value. The acquisition of the interest multiple ratio value generated by these banks was relatively stable until after the implementation of the 2020 restructuring, with a total of 27 companies recording interest multiple ratios of more than 1. Based on the average value of the interest multiples ratio generated from 2018 to 2020, namely 1.41867, 1.26545, and 1.05325, there was a decrease in the ability of the research sample companies to generate pre-tax income to pay interest costs. In connection with the decline in the profitability ratio that occurred, the recording of the declining profit was also affected and was directly proportional to the multiplied interest ratio generated.

5.2. Impact of Countercyclical Policy on Company’s Financial Performance

Based on the results of research tests, there were no significant changes to the financial performance of banks before and after restructuring. Similar results are shown in the research of [10], which states that the implementation of PT X’s debt restructuring has not been able to improve financial performance. In this case, there was no significant change in financial performance before and after restructuring. [11] Also, the liquidity value before and after credit restructuring did not change significantly. One of the things that underlies this statement is the determination of the criteria for credit relaxation set by the Financial Services Authority (OJK) in POJK No.11/POJK.03/2020 so that the implementation of credit restructuring during the COVID-19 pandemic had certain limitations, considering the sustainability of the national economy and the banks’ operations. In this case, the OJK applied criteria to business entities that are allowed to restructure credit including the tourism, transportation, hotel, food and beverage (F&B), retail, multi-finance, pharmaceutical, agriculture, mining, and automotive sectors that were affected by COVID-19. For example, debtors who can apply for credit relaxation must meet the criteria for experiencing losses in their company operations caused by COVID-19.

Not only from the debtor’s point of view, the bank as a creditor also has the right to play a role in determining the steps in the credit restructuring process. Banks can also make credit settlements if credit restructuring cannot be carried out. The settlement of bad debts or wrong debt recovery is an attempt by banks to collect bad loans that have been removed from the records. Credit settlement is one of the bank’s strategies in maximizing credit recovery, whose implementation must also go through studies such as looking at credit terms and conditions, considering debtor assets that are used as credit collateral, estimating handling time, and potential risks that arise. Banks can also make efforts to liquidate the debtor’s collateral assets if the collection’s Present Net Value (NPV) is considered lower than the asset. Thus, banks have the right to determine the steps for implementing credit restructuring policies by adjusting the conditions and considering the sustainability of the company’s business. As explained by [23], the implementation of credit restructuring has the potential to impact bank profitability, but this does not happen to all banks. For banking companies whose loans are mainly distributed in the micro, small, and medium enterprises (MSME) segment, such as PT Bank Rakyat Indonesia (Persero) Tbk, PT Bank Tabungan Negara (Persero) Tbk, and PT Bank BTPN Syariah Tbk, restructuring policies can have a more significant impact. Unlike the case with PT Bank Negara Indonesia (Persero) Tbk, whose MSME loan distribution is only around 15–20%, the implementation of the credit restructuring does not have a significant effect.

Regulations are enacted in response to a crisis that cannot be identified [50]. Therefore, the crisis that arises is motivation and creates demands for regulators to establish specific accounting policies. The regulatory theory explains that the economy used is a centralized economy to protect the public or public interest. Policies set by the legislature aim to protect users of financial institutions, which is realized by improving economic performance. Thus, regulations are set taking into account the various interests and consequences resulting from the stipulation of these regulations. Regulatory theory consists of signaling, public interest, capture, and bushfire theories. Signaling theory, also called the theory of disclosure, states that an entity can increase its firm value through financial statements. Facing an increasingly competitive capital market with increasingly intelligent and sophisticated investors triggers companies to maximize the company’s value and provide all the available information in the financial statements. Thus, these companies can be seen as better than companies that do not report their finances.

Signaling theory can be referred to as a self-regulating system or a self-perpetuating process. Signaling theory relies on the functioning of a perfect, free-market economy, in contrast to the fact that market economies are rarely perfect or free. This is because each company has its reasons for issuing its financial statements. Therefore, public interest theory states that the existence of regulations is a response to public demand to correct inefficiencies and injustices in market practice. Public interest theory is based on two assumptions: that the economic market is fragile, so it is likely to operate inefficiently and unfairly if left unregulated, and that making regulations or rules comes almost without cost.

In public interest theory, accounting standards setting responds to an inefficient financial or accounting information market. Regulations were developed in response to public demand for inefficient market conditions. Therefore, as the name implies, the preparation of regulations with public interest theory should be carried out based on the public’s wishes not on the interests of the regulator.

These results are consistent with agency theory, namely, that the differences in interests between shareholders and managers can cause managers to commit fraud in financial reports. Shareholders cannot supervise managers when they carry out their duties in managing the company, which can create opportunities for managers to commit fraud because shareholders do not know as much about a company as the manager. In addition, managers can commit fraud in manipulating financial statements for their own interests so that their performance appears better and always appears to be on target. The results of this study are consistent with the agency theory, which states that when a company’s financial stability or profitability is good, it is unlikely that the company will manipulate profits. Financial statement fraud can be carried out by presenting the company as having a higher value than it does in reality (overstatements) or a lower value than it does in reality (understatements). Examples of misstatements commonly used include overestimating the number of assets, sales, and profits and reducing the amount of debt, expenses, and losses [51] (Financial statement fraud is also carried out to attract the attention of investors’, obtain a higher selling price for acquisitions, achieve company goals, eliminate negative perceptions in the market, and receive higher compensation for good performance [52]. According to [53] the signs of fraudulent financial statements include the existence of accounting anomalies, weak internal controls, analytical anomalies, excessive lifestyles, unusual behavior, and complaints.

5.3. The Effect of Restructuring Policy

Supported by research conducted by [24], it was believed that the implementation of restructuring could cover the risk of potential losses and improve the quality of bank assets by reducing the risks from non-performing loans. In conclusion, the credit restructuring policy regulated by the Financial Services Authority (OJK) considered the consequences that may arise from the bank as the executor having the authority to assess the feasibility of debtors affected by the COVID-19 pandemic to have the right to reschedule, recondition, and restructure. This was also done to minimize the risk of bad credit or default by debtors affected by COVID-19 had there been no concessions. Therefore, the credit restructuring policy also helps the performance of banks by avoiding the risk of bad loans. In addition, limiting the criteria for debtors who can carry out credit restructuring also keeps bank liquidity stable while still obtaining credit payments from debtors who are not affected by the COVID-19 pandemic. These things can be the reason for the insignificant change in the company’s financial performance before and after the restructuring.

5.4. Summary of Findings

The research findings of this study as follows:

(1)

The company’s financial performance was measured by the current ratio, operating profit margin, and time interest earned ratio. The results of the current ratio study show that a ratio of more than one indicates that there is no significant liquidity problem. This proves that the bank subsector companies in the period before the implementation of the countercyclical policy tend to be able to make payments on their current liabilities with their current assets. Operating profit margin with an average operating profit margin ratio for all the research samples, continued to decline from year to year. It can be concluded that there was a decline in the profitability of the banking sub-sector companies not only after the countercyclical policy was implemented but also in 2019. The decline in banking profitability that occurred in 2019 was felt by almost all companies in the banking industry due to the lack of credit distribution. This caused a decrease in the companies’ net profits. The time interest earned ratio results indicate a decrease in the ability of the research sample companies to generate pre-tax income to pay interest costs. In connection with the decline in the profitability ratio that occurred, declining profit was also affected and was directly proportional to the multiplied interest ratio generated.

(2)

There were no significant changes in the banks’ financial performance before and after the restructuring. This shows that the criteria for credit relaxation set by the Financial Services Authority (OJK) in Financial Services Authority Regulation (POJK) No.11/POJK.03/2020 were effective. The implementation of credit restructuring during the COVID-19 pandemic had limitations, which have been regulated by considering the sustainability of the national economy and the banks’ operations. In this case, the OJK applied the criteria to business entities that were allowed to restructure credit including the tourism, transportation, hotel, food and beverage (F&B), retail, multi-finance, pharmaceutical, agriculture, mining, and automotive sectors that were affected by COVID-19. Debtors who could apply for credit relaxation also had to meet the criteria for experiencing losses in their company’s operations caused by COVID-19. Not only from the debtor’s point of view but also the bank as the creditor had a right to play a role in determining the steps in the credit restructuring process. Restructuring). Settlement of bad debts or wrong debt recovery is an attempt by banks to collect bad loans that have been removed from the records. The credit restructuring policy regulated by the OJK considered the consequences that could arise from giving the banks the authority as the executor to assess the eligibility of debtors affected by the COVID-19 pandemic to obtain the right to reschedule, recondition, and realign. Therefore, the credit restructuring policy also helped the performance of banks to be free from the risks of bad loans. This could be the reason for the insignificant changes in companies’ financial performance before and after the restructuring.

(3)

The study results show that several companies manipulated or created fraudulent financial statements, namely, one company classified as a manipulator in 2018 and two companies in 2020. Further research findings found that more than half the companies in the research sample were classified as non-manipulator companies each year. This shows that these companies have been responsible by presenting reliable financial statements or not carrying out any manipulations.

6. Conclusions

Based on the results of the analysis and discussion related to the impact of the implementation of countercyclical policies on companies’ financial performance and the detection of fraud in companies’ financial statements using the Beneish M-score model, the following conclusions can be made.

First, the impact of countercyclical policy implementation on financial performance. The countercyclical policy is a buffer put in place to provide additional capital to compensate for losses in the event of excessive credit growth that has the potential to disrupt financial stability. This policy encourages the optimization of the banking intermediation function, maintains financial system stability, and supports economic growth. The spread of COVID-19 has had both a direct and an indirect impact on the financial performance of banks, with the potential to disrupt financial system stability that can affect economic growth. Analysis of the profitability, liquidity, and solvency ratios of 32 companies before and after implementing the credit restructuring policy showed no significant changes in the financial performance of these companies. Implementing a countercyclical policy in credit restructuring for banking companies impacted the company’s overall profit recording, which experienced a decline. However, the decline in the companies’ profit components did not fully encourage or motivate companies to manipulate financial statements, as evidenced by the Beneish M-score model analysis. The findings of this study indicate that regulatory theory, consisting of signaling theory, public interest theory, capture theory, and bushfire theory, cannot be proven empirically in the sample of this study. This study proves that overall, company profits have decreased for banks that implemented credit restructuring policies during the COVID-19 pandemic. Based on the results of this study, bank managers should be required to have qualified crisis management strategies and be more sensitive crises so that should something similar happen in the future, they would be better prepared to handle it.

Moreover, such strategies and steps can maintain the financial stability of the banking system so that it does not experience shocks or declines. In preventing the manipulation of financial statements, a supervisory role by the respective boards of commissioners and audit committees is also needed. The audit committee assists the board of commissioners in fulfilling their oversight responsibilities on the financial reporting process and the bank’s internal control system. To minimize the potential manipulation of financial statements, banks should make efforts not only in the form of prevention but in combination with efforts to detect, investigate, and improve systems to create an integrated strategy for controlling fraud. Prevention is the first pillar of an anti-fraud strategy that aims to stop fraud from occurring. Fourteen fraud prevention measures are grouped into three main categories, namely, creating a culture of honesty and high ethics, evaluating the implementation and control of anti-fraud strategies, and developing appropriate monitoring processes. Creating a culture of honesty and high ethics includes creating a positive work environment; establishing a company code of ethics; taking consistent action in dealing with fraud; creating an ethical atmosphere in the workplace; conducting fraud detection training for employees and management; and conducting investigations to find out the background of employees. Evaluating anti-fraud processes and controls includes implementing and monitoring preventive and detective internal controls; identifying and measuring fraud risks; and making changes to company processes and activities to reduce or eliminate the risk of fraud. Developing appropriate monitoring processes includes ensuring the effectiveness of the internal audit function; streamlining the function of the board of commissioners or audit committee in a supervisory function; streamlining management in a supervisory function; having an internal and external audit team that has a certified fraud examiner; and involving external auditors to identify fraud.

Second, the fraud detection of financial statements using the Beneish M-score model. The analysis of the results of the Beneish M-score model conducted on financial statement data from 2018 to 2020 showed that companies that implemented credit restructuring policies did not experience a tendency to manipulate or create fraudulent financial statements. The credit restructuring policies carried out by the banks including (1) reducing loan interest rates; (2) extending credit periods; (3) reducing loan interest arrears; (4) reducing principal loan arrears; (5) adding credit facilities; and/or (6) converting credit into temporary equity participation. In credit restructuring, each bank or finance company should find the best solution for the debtor by considering the impact of COVID-19 on the debtor’s business, the ability to pay debtors, and the ability of each individual bank or finance company. Considering the increase in the number of non-manipulator companies in 2020 and considering the results of the analysis that there was no significant effect the companies’ profitability, liquidity, and solvency ratios before and after the implementation of countercyclical policies, it can be concluded that the implementation of credit restructuring in banking companies does not encourage the manipulation of financial statements.

Nevertheless, fraudulent financial statements still have the potential to occur in companies classified as manipulators and gray companies. Companies classified as gray companies have been shown to manipulate financial statements, although not in significant amounts. Similar indications appeared for manipulator companies after the restructuring was implemented.