THE NECESSITY OF THE STUDY
Fraud is a deliberate act that benefits the perpetrator while harming others involved One common form of fraud in the corporate world is fraudulent financial reporting, which involves manipulating material information in financial statements to mislead users, often to attract investment Such fraudulent practices occur not only in individual companies but also on a global scale, leading to widespread implications for the financial industry.
The 2020 Global Study on Occupational Fraud and Abuse by ACFE reveals that while asset misappropriation (74%) and corruption (51%) dominate fraud cases in the Asia-Pacific region, financial statement fraud, despite accounting for only 14% of cases, results in a significantly higher median loss than the combined losses from asset misappropriation and corruption.
Public companies experienced the highest median losses from fraudulent financial reporting, averaging $3,000,000, closely followed by private companies The report highlighted that the banking and financial services sector faced the most significant fraud cases, accounting for 19% of incidents, compared to the 8% in other sectors This underscores the urgent need for enhanced fraud prevention measures in these industries.
Bank-related fraud scandals have significantly impacted the financial sector globally, with notable examples including the Wells Fargo scandal, where 3.5 million fake customer accounts were created, leading to unauthorized automobile insurance charges for over 800,000 borrowers from 2016 to 2020 Similarly, British banks faced repercussions from the Payment Protection Insurance (PPI) scandal, resulting in Barclays, HSBC, Lloyds, and Royal Bank of Scotland collectively paying $17.6 billion for the improper sale of PPI in 2012 In Vietnam, the Huynh Thi Huyen Nhu scandal involved the embezzlement of over 4,911 billion dong from VietinBank through falsified records and high-interest public capital solicitation, affecting multiple banks and companies Additionally, Pham Cong Danh's actions at the Vietnamese Construction Bank (VNCB) led to unlawful practices that caused damages exceeding 18,000 billion dong, revealing a network of complicity among higher-level officials These incidents highlight the pervasive issue of fraudulent financial reporting within the banking industry.
This study employs pentagon theory to enhance the prevention and detection of fraudulent financial reporting, utilizing the F-score model as a measurement tool for assessing risk levels By implementing these strategies, it aims to improve the accuracy of detecting fraudulent activities in banking and financial reporting.
RESEARCH OBJECTIVES
This study investigates the impact of five key elements from the pentagon fraud theory—pressure, opportunity, competence, arrogance, and rationalization—on various financial and organizational factors The research explores related hypotheses including Financial Target, Financial Stability, External Pressure, Institutional Ownership, Company Growth, Liquidity, Ineffective Monitoring, Nature of Industry, Change of Auditor, Total Accruals, and Quality of External Auditor.
Change of directors, and CEO Picture on fraudulent financial statements in banking and financial sector companies listed on the Ho Chi Minh Stock Exchange.
RESEARCH QUESTIONS
This study aims to explore the application of the pentagon theory in detecting fraudulent financial reporting through the F-score model, specifically within banking and financial sector companies listed on the Ho Chi Minh City Stock Exchange.
RESEARCH METHODOLOGY
The author uses logistic regression analysis as a research methodology of data analysis with binary variable is fraudulent financial statement.
SUBJECT AND SCOPE OF THE STUDY
This study draws inspiration from prior research on international students from various countries, including Indonesia, Malaysia, Nigeria, and India It focuses on the impact of five key elements derived from the pentagon fraud theory to detect financial reporting fraud.
Numerous prior studies have explored this topic across various sectors, including banking, finance, manufacturing, technology, government, public administration, energy, and retail This research focuses specifically on banking and financial sector companies listed on the Ho Chi Minh Stock Exchange, accessible through its official website, HOSE (hsx.vn).
CONTRIBUTIONS OF THE STUDY
This study will concentrate on forth primary contributions:
Firstly, to research academics: This research is meant to be able to contribute theory development in auditing
Secondly, to company: This research is meant to be able to contribute to making policies regarding fraudulent financial reporting prevention
Thirdly, to CPA firms: This research is meant to be input and consideration in taking actions counting the fraudulent financial reporting prevention
Fourth, to shareholders: This research is meant to provide information or users as to financial statements so as to understand raud-causing factors without meeting mistakes in making decision.
THE INNOVATIVE
The author introduces groundbreaking research focused on the banking and financial sector in Vietnam, marking it as the first of its kind in this context Unlike previous studies that primarily examined overseas students in countries such as Indonesia, India, Nigeria, and Malaysia, this research incorporates new independent variables related to the pentagon fraud theory These variables include Institutional Ownership, Company Growth, Liquidity, Nature of Industry, Total Accruals, and External Quality of Auditors, providing a fresh perspective on the subject.
THESIS STRUCTURE
Chapter 3: Research Method, Research Model And Research Hypotheses; Chapter 4: Results And Analysis;
RELATED CONCEPTS
Fraud is defined as intentional deceptive behavior aimed at unlawfully obtaining property or rights from another individual or entity It manifests in various forms, including bankruptcy fraud, credit card fraud, securities fraud, tax fraud, and wire fraud The complexity and value of the assets involved often determine the nature of the fraudulent activity, which can be perpetrated by individuals, groups, or entire businesses.
Fraud in finance manifests in various forms, including insurance claims, cooking the books, pump and dump schemes, and identity theft leading to unauthorized purchases Individual mortgage fraud schemes often involve identity theft and falsification of income or assets, while industry experts may engage in appraisal fraud and air loans to exploit the system Common types of investor mortgage fraud include occupancy fraud, property flipping, and straw buyer scams In the insurance sector, perpetrators may exploit lengthy claim investigations to file small, fraudulent claims, knowing that insurers are likely to approve them without thorough scrutiny This manipulation of facts involves withholding crucial information or providing misleading statements to achieve financial gain Ultimately, successful fraud relies on the perpetrator's ability to leverage information asymmetry, where the cost of verifying information discourages full investment in fraud prevention measures.
In auditing financial statements, fraud refers to the deliberate misrepresentation of financial information, primarily categorized into fraudulent financial reporting and asset misappropriation These categories highlight the auditor's responsibility to identify material misstatements effectively.
Fraudulent financial reporting involves the deliberate misrepresentation or omission of financial information to mislead users Typically, this includes the intentional misstatement of amounts, such as when WorldCom improperly capitalized billions in expenses as fixed assets Although less frequent, omissions can also occur, allowing a company to inflate income by excluding accounts payable and other liabilities.
Fraudulent financial reporting often involves overstating income through inflated assets or omitted liabilities, but companies may also intentionally understate income, particularly privately held firms seeking to lower tax liabilities This practice can create "cookie jar reserves" to boost future earnings, a strategy known as income smoothing and earnings management Earnings management encompasses deliberate actions by management to achieve earnings targets, while income smoothing specifically involves shifting revenues and expenses across periods to minimize earnings volatility Techniques for income smoothing include reducing the value of inventory and other acquired assets to enhance future earnings upon their sale, as well as overstating inventory obsolescence reserves and allowances for doubtful accounts to offset higher earnings.
Notable cases of fraudulent financial reporting often stem from inadequate disclosure, as exemplified by the Enron scandal, where the company failed to properly disclose obligations to special purpose entities Similarly, E F Hutton, a defunct brokerage firm, was charged with intentionally overdrawing accounts to inflate interest earnings, yet the balance sheet's description of these liabilities lacked clarity regarding their true nature.
Fraudulent financial reporting often arises from earnings management, where management alters accounting policies or estimates to present improved results Factors influencing these unethical behaviors include personal incentives, market pressures, a lack of ethics, and attempts to meet financial analysts' projections, ultimately affecting stock prices For instance, a senior accountant may manipulate expenses and liabilities to enhance perceived performance, misleading investors about the company's debt situation Additionally, accountants might misclassify operating expenses to inflate net income or exaggerate projected earnings growth, such as a sudden jump in profit margin estimates These practices not only misrepresent the company's financial health but also deceive investors, highlighting a serious lack of business ethics and corporate governance.
Fraudulent financial reporting faces scrutiny from external audits, regulatory frameworks, and independent boards of directors However, the cornerstone of accurate financial reporting lies in fostering an ethical corporate culture within the organization.
Fraud will always exist without effective prevention and detection strategies Conflicts often arise between these two aspects in financial statements, influenced by various motivating factors that lead to fraud To explain this relationship, Crowe’s Fraud Pentagon Theory was developed, building upon earlier concepts like Cressey’s Fraud Triangle Theory and Wolfe's Fraud Diamond Theory.
In 1953, Cressey was an originator who laid the groundwork for the fraud theory with
The fraud triangle theory, reintroduced by Wells in 1997, identifies three key elements: pressure, opportunity, and rationalization, which contribute to fraudulent behavior According to Hunton et al (2004), fraud arises when these elements interact, with opportunity stemming from inadequate internal controls or collusion that enables perpetrators to evade detection Pressure, which can arise from various sources such as medical expenses, a costly lifestyle, or addiction issues, serves as the driving force behind the decision to commit fraud Understanding these components is essential for preventing and addressing fraudulent activities.
In 2004, Wolfe and Hermanson introduced the fraud diamond theory in the CPA Journal, expanding upon the existing fraud triangle theory by adding a fourth element: capability They argued that while pressure and opportunity may facilitate fraud, it is the presence of capability that ultimately enables it to occur This means that a potential perpetrator must possess the necessary skills and abilities to exploit opportunities for fraud As noted by Wolfe and Hermanson, "Opportunity opens the doorway to fraud, and incentive (i.e., pressure) and rationalization can draw a person toward it However, the person must have the capability to recognize the open doorway as an opportunity and to take advantage of it by walking through, not just once, but repeatedly." This additional element of capability sets the fraud diamond theory apart from Cressey's original fraud triangle theory.
In 2011, the fraud pentagon theory developed by Crowe Horwath introduced two new elements—competence and arrogance—building upon previous fraud theories Competence refers to an employee's ability to bypass internal controls and devise concealment strategies for personal gain, while arrogance reflects a sense of superiority that leads individuals to believe that company policies do not apply to them Jonathan Marks, in 2012, emphasized the evolution of business environments from the 1950s to the 2000s, noting that companies have transformed from local operations with simple structures to global enterprises with complex networks and delegated management Marks highlighted that 89% of fraud cases involve top-level executives, such as CEOs and CFOs, with 70% of perpetrators motivated by arrogance, greed, and personal pressure He concluded that the level of arrogance among company leaders significantly influences fraudulent behavior.
A personality that has a high ego and has a mindset of ‘CEO is celebrity’
Having the idea that the application of internal control cannot prevent fraud activities that they do
Having behaviors that tends to often intimidate subordinates and/or coworkers
Having an autocratic management style
Having fears of losing position and/or status that has been achieved[6]
It comes down to which elements belong to Crowe’s Fraud Pentagon theory include Pressure, Opportunity, Competence, Arrogance, Rationalization
Conflicts of interest in the business world often lead to fraud, as individuals prioritize personal gain over professional responsibilities This issue is exemplified by the agency problem, where a misalignment of interests occurs between principals (owners) and agents (managers) Proposed by Michael C Jensen and William H Meckling in 1976, agency theory highlights the challenges posed by information asymmetry, which can result in agents engaging in earnings management to maximize their utility Such agency problems are prevalent in fiduciary relationships, where corporate leaders may not share the same motivations as investors, leading to potential fraudulent activities aimed at misleading shareholders A notable case is Enron, where the board's failure to fulfill its oversight responsibilities contributed to an accounting scandal that resulted in significant financial losses and the company's eventual collapse The fraud pentagon theory identifies five elements—pressure, opportunity, competence, arrogance, and rationalization—that can help detect fraud, emphasizing the risks associated with differing goals among stakeholders.
THEORETICAL FRAMEWORK
2.2.1 Effects of Pressure on Fraudulent Financial Reporting
The initial factor in the pentagon fraud model is pressure, which can stem from both external and internal sources When a company faces financial instability, this pressure can lead to the temptation to produce fraudulent financial statements.
According to Hery (2016), entities may feel pressured to manipulate financial statements when faced with declining or unstable financial prospects due to various economic, industrial, or operational conditions This pressure can stem from unmet goals or time constraints, prompting employees to engage in fraudulent financial reporting (Auditor of Public Accounts, 2011).
Picture 2 Crowe's Fraud Pentagon that, but also companies, when conducting their business activities, often use not only funds obtained from investors, but also money from creditors in the study Analisis pengaruh faktor risiko kecurangan terhadap manajemen laba (Rachmasari, P., &
Darsono (2015))[15] In Other People's Money: A Study in the Social Psychology of
Embezzlement of Donald R Cressey also states that The presence of pressure could lead to committing fraud[16]
Rukmana (2018) highlights that individuals may resort to fraudulent activities to alleviate pressures, such as the need to meet financial targets, ultimately impacting financial statement integrity and firm value in Indonesia.
Determination of Fraudulent Financial Reporting Causes by Using Pentagon Theory
In Indonesian manufacturing companies, individuals may resort to fraudulent activities to alleviate pressures associated with meeting financial targets This pressure often stems from the desire to secure bonuses and higher income through maximum performance Unfortunately, such performance can be compromised by opportunistic behaviors, including financial statement fraud This issue is further explored in the study "Analisis Pengaruh Faktor Risiko Kecurangan Terhadap Manajemen Laba" by Rachmasari and Darsono.
In 2015, it was emphasized that when management secures a loan, they must enter into a contractual agreement that clearly outlines the terms, particularly the principal amount and interest obligations This loan arrangement often places pressure on management from external creditors, as discussed in the journal "Analysis of Fraudulent Financial Statement: The Fraud Pentagon Theory Approach" by Ratnasari and Solikhah (2019).
High levels of pressure significantly increase the likelihood of fraudulent behavior (Albrecht et al., 2008) This positive correlation between pressure and fraud is supported by various research studies.
The impact of Pentagon Fraud on financial statement fraud and firm value in Indonesia has been explored through various studies, including Rukmana's 2018 research Additionally, Quraini and Rimawati (2018) identified key determinants of fraudulent financial reporting Rengganis (2019) emphasized the significance of the Fraud Diamond in detecting financial statement fraud Lestari and Henny (2019) analyzed the influence of Pentagon Fraud on fraudulent financial statements within banking companies listed on the Indonesia Stock Exchange from 2015 to 2017 Furthermore, Setiawati and colleagues conducted a case study on detecting fraudulent financial reporting using the Fraud Pentagon analysis in manufacturing firms listed on the BEI from 2014 to 2016.
This first element is developed by hypotheses regarding Financial Target, Financial Stability, External Pressure, Institutional Ownership, Company Growth, Liquidity, six of which are discussed in the chapter 3
2.2.2 Effects of Opportunity on Fraudulent Financial Reporting
Opportunity arises when weak controls enable management to engage in fraudulent financial reporting This condition creates a favorable environment for individuals to commit fraud, highlighting the importance of robust oversight to mitigate such risks.
The analysis of the impact of the Fraud Pentagon on financial statement fraud, utilizing the Beneish Model, highlights that perpetrators often exploit opportunities due to a belief that their actions will remain undetected Such opportunities typically arise in companies with weak internal control systems, insufficient management oversight, lax penalties, and ambiguous procedures This is further supported by research on the Fraud Pentagon in earnings management within the metal and chemical manufacturing sectors.
V., & Saphira, J (2019) ) states that opportunity is an opportunity that someone has to take actions that are contrary to company regulations or policies[15] The study Other
People’s Money: A Study in the Social Psychology of Embezzlement of Cressey, D
According to research conducted in 1953, increased opportunities can lead to a higher likelihood of committing fraud A subsequent study, "An Analysis of Student’s Academic Fraud Behavior" (Muhsin, 2018), supports this finding by indicating that broader opportunities significantly elevate the chances of individuals engaging in fraudulent activities.
Agency theory posits that fraud may arise from conflicts of interest between the principal and the agent When there is alignment of interests, agents typically act as expected without supervision However, agents' self-interest can lead to uncertainty for the principal regarding adherence to contractual agreements Effective supervision is essential, as a lack of oversight can allow agents to prioritize personal gains Additionally, agents possess more information than principals, creating information asymmetry that complicates the principal's ability to monitor the agent's actions This dynamic can prompt agents to present misleading information Research by Summer and Sweeney, as cited in Sihomding and Rahardjo (2014), indicates that the assessment of accounts receivable and inventory involves subjective judgment, which management can exploit to manipulate financial statements.
The previous researches pointed out that this second element has a positive effect on fradulent financial reporting such as: The Analysis of Fraudulent Financial Reporting
The Fraud Pentagon Approach has been utilized in various studies to analyze fraudulent financial reporting, highlighting its significance in identifying deceptive practices in manufacturing companies listed on the IDX from 2014 to 2016 (Setiawati & Baningrum, 2018) Research by Apriliana and Agustina (2017) emphasizes the determinants of fraud through this framework, while Nanda, Zenita, and Salmiah (2019) further explore the implications of fraudulent financial reporting using the Fraud Pentagon model Additionally, an analysis of academic fraud behavior among students (Muhsin, 2018) provides insights into broader fraud dynamics Rukmana (2018) examines the impact of the Fraud Pentagon on financial statement fraud and its subsequent effect on firm value in Indonesia, underscoring the model's relevance in both corporate and educational contexts.
This second element is developed by hypotheses regarding Ineffective monitoring, and Nature of Industry, two of which are discussed in the chapter 3.
2.2.3 Effects of Rationalization on Fraudulent Financial Reporting
Rationalization is the existence of thoughts that can make a person to justify his actions even if the actions are wrong The journal Analisis Pengaruh Fraud Pentagon
The article discusses financial statement fraud and the application of the Beneish Model in companies implementing the Aseac Corporate Governance Scorecard It highlights that individuals who engage in fraudulent activities often seek rationalizations for their behavior Additionally, the study examines the quality of internal control procedures as a key factor in preventing such fraud.
Moderating Effect on Organizational Justice and Employee Fraud (Rae, K., &
According to Subramaniam (2008), rationalization is the third element of the fraud pentagon theory, representing the justification of fraudulent actions stemming from an employee's lack of personal integrity or other moral considerations This concept is explored in the journal "Fraud Prevention Initiatives in The Nigerian Public Sector," which examines the connection between fraud incidence and the components of the fraud triangle theory (Abdullahi & Mansor).
PREVIOUS RESEARCH
Author Year Method Dependent variable
Financial Target, Financial Stability, External Pressure, Institutional Ownership, Ineffective Monitoring, Change in Auditor, Change of Directors, and Frequent Number of CEO’s picture
Financial stability, external pressures, and the frequent turnover of CEOs significantly impact fraudulent financial reporting, while other independent variables show no discernible effect.
Financial Target, External Pressure, Ineffective Monitoring, Changes in Auditor, Changes of Board of Directors, and Frequent number of CEO picture
All of 6 independent variables have no significant effect on the fraudulent financial reporting
Mukhtaruddin, Evlin Sabrina, Arista Hakiki, Yulia
Financial Target, External Pressure, Ineffective Monitoring, Changes in Auditor, Changes of Board of Directors, and Frequent number of CEO picture
All of 6 independent variables have no significant effect on the fraudulent financial reporting
Finanical Target, Financial Stability, External Pressure, Ineffective Supervision, Change of External Auditor,
Financial Stability and the Frequency of CEO photo appearance have a significant influence on
Change of Directors, Auditor Opinion, Frequency of CEO photo appearance, and politician CEO fraudulent financial reporting, meanwhile the rest have no influence on this report
NAOMI CLARA SITUNGKIR, and DEDIK NUR TRIYANTO
Finanical Stability, External pressure, Nature of Industry, Effective of Rationalization, Change in Auditor, Total Accrual, Capability, Change of Directors, Ineffective of Monitoring, Frequent
Finanical Stability and Family Firms have a positive effect on fraudulent financial reporting, External pressure and Total Accruals have a negative effect on this report number of CEO’s pictures, Family firms
The rest of independent variables have no effect on this report
Table 8.Overview of previous researches
In this chapter 2, the author went defining the concepts regarding Fraud,
The article explores fraudulent financial reporting through the lens of the Pentagon fraud theory and Agency theory It details the five key elements of the Pentagon fraud theory, supported by prior research that yielded positive findings, and identifies relevant proxies for each element Additionally, the author reviews previous studies that serve as a foundation for developing their thesis Following this overview in Chapter 2, the author will advance to Chapter 3 for a more in-depth analysis of their research.
RESEARCH FRAMEWORK
A general review of previous researches
RESEARCH METHOD
Data collection method: Quantitative research
Quantitative research is a systematic approach that involves the collection of quantifiable data to analyze phenomena through statistical, mathematical, or computational techniques This method focuses on gathering information from existing and potential sources, making it highly data-oriented In this study, the author utilized secondary data obtained from audited financial reports and annual reports in the banking and finance sectors, sourced from Hose's official website using a purposive sampling technique This data collection approach is commonly employed in research related to this field.
RESEARCH MODEL: F-SCORE MODEL
The F-score model, created by Patricia M Dechow, Weili Ge, Chad R Larson, and Richard G Sloan in 2011, serves as a probability-oriented metric to assess the likelihood of material accounting misstatements within a firm's financial statements Since many material misstatements often stem from fraudulent activities, the F-score is recognized as an effective tool for identifying fraudulent financial reporting.
In 2011, three models were developed that utilized financial statement data, non-financial variables, and market data, with the first model relying solely on financial statements to calculate the F-score An F-score of 1 indicates a neutral probability of misstatements, while a score above 1 suggests a higher likelihood of financial inaccuracies, and a score below 1 indicates a lower risk Additionally, an F-score below 1 may signal potential fraud in financial statements Research indicates that F-score models outperform other fraud detection models; for instance, Aghghaleh et al (2016) found that the Beneish M-score had an average accuracy of 73.17%, while the Dechow F-score achieved 76.22% accuracy in detecting fraudulent financial reporting among Malaysian companies from 2001 to 2014 Furthermore, a study in Vietnam demonstrated that the F-score model had a fraud predictive capacity of approximately 78%.
The F-score model is highlighted as an effective tool for measuring financial statement fraud, making it a recommended first-pass screening method for auditors to detect material misstatements in a company's financial statements This study supports the use of the F-score model as a valuable choice for assessing financial integrity.
In this study, the author follow this model with the equation as follows:
HYPOTHESIS DEVELOPMENT
3.4.1 Pressure is proxied by six following independent variables:
To achieve planned financial targets, company managers must perform at their best, as highlighted in the journal "Detecting and Predicting Financial" (Skousen, JC, Wright, JC, Smith Kevin, R 2009) A key metric for evaluating operational performance is the Return on Assets (ROA), which compares income to total assets, demonstrating the efficiency of asset utilization This financial target plays a crucial role in assessing managerial performance and influences decisions regarding bonuses and wage increases.
Return on Assets (ROA) is a key financial metric that measures a company's profitability in relation to its total assets It provides insights into the efficiency of a company's management in utilizing its assets to generate earnings Expressed as a percentage, a higher ROA indicates better performance and profitability.
Detecting financial statement fraud is closely linked to a company's value, as highlighted by Heru Satria Rukmana in 2021 Return on Assets (ROA) serves as a key indicator of management's effectiveness in generating overall profits A higher ROA signifies greater profitability and indicates a more efficient use of assets Research shows that companies with substantial profits, as measured by ROA, are more prone to engage in earnings management compared to those with lower profit margins.
Higher profit targets can significantly increase the pressure on company management, leading to a heightened sense of urgency to achieve these goals This pressure may compel leadership to prioritize profit over ethical considerations, potentially compromising their decision-making processes As companies aim for elevated Return on Assets (ROA), they may inadvertently increase their risk of financial misconduct The study "Detecting and Predicting Financial Statement Fraud: The Effectiveness of the Fraud Triangle and SAS No 99" highlights the implications of such pressures on financial integrity.
According to Wright (2009), financial targets imposed by a company's board of directors create pressure on managers to meet these goals, which can lead to the manipulation of financial reports Supporting this notion, Ely Indriyani and Dhini Suryandari (2021) highlight that financial instability, as explored through the lens of pentagon theory, places additional stress on management, potentially resulting in fraudulent activities to maintain the appearance of meeting financial objectives This relationship underscores the connection between agency theory and the fraud pentagon theory, illustrating how unstable company conditions can hinder investment and operational flow, thereby increasing the risk of financial misconduct.
Former researches’ result brought a positive effect on fraudulent financial reporting with Financial target (ROA) such as: the journal Analisis Pengaruh Fraud Pentagon Terhadap Fraudulent Financial Reporting Menggunakan Beneish Model
(Jaunanda, M., Tian, C., Edita, K., and Vivien (2020)), Fenomena Kecurangan Laporan
The financial landscape of publicly traded companies in Indonesia is significantly influenced by fraud detection mechanisms Research highlights the impact of the fraud triangle on identifying financial statement fraud within manufacturing firms listed on the Indonesia Stock Exchange (IDX), as discussed by Santoso (2019) and Widarti (2015) Additionally, an empirical analysis of the fraud diamond framework offers insights into effectively detecting financial statement fraud among these companies during the 2013-2015 period.
2015 (Putriasih, d (2016)), Analysis of Fraud Pentagon to Detecting Corporate Fraud in Indonesia (Christian, N., Basri, Y Z., & Arafah, W (2019))
Based on the description and results of previous research, the proposed hypothesis is:
H1: Financial Target has a positive effect on fraudulent financial reporting
The journal "Fraud Detection of Financial Statements through Pressure and Opportunity Risk Factors" (Rahmanti & Daljono, 2013) highlights that Financial Stability is essential for effective economic mechanisms, including pricing, fund allocation, and risk management, which collectively foster economic growth and protect companies from monetary crises.
Financial stability, as defined by the World Bank, is essential for a well-functioning financial system that efficiently allocates resources, manages risks, and maintains employment levels A stable financial system can absorb shocks through self-corrective mechanisms, preventing disruptions to the real economy The importance of financial stability becomes evident during periods of instability, where banks hesitate to finance viable projects, asset prices become misaligned with their true values, and payment delays occur Such instability can lead to severe consequences, including bank runs, hyperinflation, and stock market crashes, ultimately undermining confidence in the financial and economic systems.
The journal Fraud in Emerging Markets: A cross country analysis (Skousen, CJ,
Research by Twedt and BJ (2009) indicates that companies experiencing growth below the industry average are likely to manipulate financial reports to enhance their perceived prospects Additionally, they found a correlation between the ratio of changes in total assets and the likelihood of financial statement fraud Supporting this, Ely and Dhini (2021) explored the detection of fraudulent financial statements through pentagon theory, highlighting the role of the audit committee as a moderating factor.
The connection between financial stability and agency theory highlights that an unstable company environment places significant pressure on management, potentially disrupting cash flow and investment opportunities, which may lead to increased fraudulent activities This scenario aligns with the principles of the fraud pentagon theory Additionally, research, such as the journal "Detecting and Predicting Financial Statement Fraud," emphasizes the effectiveness of the fraud triangle and SAS No in identifying and preventing financial fraud.
Unstable business conditions create significant pressure on management, as poor performance can impede future investment opportunities (Skousen, Smith, & Wright, 2009) Companies typically aim to improve or at least maintain stable financial conditions, as investors are more inclined to invest in firms with stable finances due to perceived lower risks This pressure to uphold financial stability can lead management to engage in unethical practices, such as fraudulent financial reporting, especially during periods of instability.
Previous research indicates a positive relationship between the fraud diamond framework and the detection of financial statement fraud, particularly in the context of financial stability A study conducted on retail companies listed on the Indonesia Stock Exchange from 2014 to 2016 highlights the effectiveness of this model in identifying fraudulent financial reporting practices (Sihombing, K S., & Rahardjo, S.).
N (2017)), Pengujian teori fraud pentagon terhadap fraudulent financial reporting
(Bawakes, H F., Simanjuntak, A M., & Daat, S C (2018)), Detecting and Predicting
Financial Statement Fraud: The Effectiveness of The Fraud Triangle and SAS No 99
(Skousen, C J., Wright, C., & Kevin, R (2009)), Analysis of Fraud Pentagon to Detecting Corporate Fraud in Indonesia (Christian, N., Basri, Y Z., & Arafah, W
(2019)), Analysis of the Effect of Diamond Fraud in Detecting Financial Statement
Fraud: Empirical Study in Manufacturing Companies Listed in Indonesia Stock Exchange (Idx) 2010 –2017 ( Syahputra, E., & Erlina (2019))
Based on the description and results of previous research, the proposed hypothesis is:
H2: Financial Stability has a positive effect on fraudulent financial reporting
The journal Fraud in Emerging Markets: A Cross Country Analysis (Skousen, CJ,
Companies often face external pressures to secure additional debt or financing to maintain competitiveness, particularly for research and development or capital expenditures This need for external funding is closely tied to the cash generated from their operating and investing activities To attract investors and obtain necessary funds, companies must demonstrate strong financial performance and favorable profit ratios, while also instilling confidence in their ability to repay any loans received.
The leverage ratio is a critical financial metric that indicates the extent of a company's reliance on debt to finance its operations, reflecting external financial pressure It assesses the balance between equity and debt, providing insights into a company's ability to meet its financial obligations Understanding a company's debt levels is essential, as excessive debt can pose risks to both the company and its investors, potentially leading to credit downgrades Conversely, if a company can generate returns exceeding its loan interest rates, debt can be a catalyst for growth However, a lack of debt might signal operational challenges, suggesting that the company may be hesitant or unable to borrow due to tight operating margins.
External pressure from debt obligations can lead managers to commit fraud, particularly in companies with high leverage and debt covenants This financial strain often motivates management to manipulate earnings to meet expectations The study "Detecting and Predicting Financial Statement Fraud: The Effectiveness of The Fraud Triangle and SAS No 99" highlights these dynamics.
DADA ANALYSIS METHOD
Descriptive statistics provide concise coefficients that summarize a data set, representing either an entire population or a sample They are categorized into measures of central tendency, such as the mean, median, and mode, and measures of variability, which include standard deviation, variance, minimum and maximum values, kurtosis, and skewness Essentially, descriptive statistics offer a clear overview of a data set's characteristics, enabling better understanding through succinct summaries of the sample and its measures The most recognized forms of descriptive statistics are the measures of center: mean, median, and mode.
Pressure Financial Target (+) Financial Stability(+) External pressure(+) Institutional Ownership(+)
Company Growth(+) Liquidity(+) Opportunity Ineffective Monitoring(+)
Nature of Industry(+) Rationality Change of auditor(+) Total Accruals(+)
Competence Quality of external auditor(+)
This study utilizes a quantitative data set categorized into four measurement scales: nominal, ordinal, interval, and ratio The author specifically excludes the nominal and ratio scales from consideration in the hypothesis development process.
3.3.1.1 Descriptive analysis for variables with ratio scale
The Ratio Scale is a comprehensive measurement scale that ranks variables, reveals the differences between them, and incorporates a true zero point, allowing for precise calculations This scale assumes that variables can have a zero value, maintaining consistent differences and a specific order among them It enables various inferential and descriptive analyses, providing detailed insights through statistical techniques such as mean, median, and mode Examples of ratio scales include weight and height, and they are essential in market research for assessing metrics like market share and sales figures The ratio scale encompasses the characteristics of nominal, ordinal, and interval scales, ensuring that variables can be labeled, ordered, and measured with equal intervals Importantly, the presence of a true zero means that ratio scales do not include negative values Researchers should utilize a ratio scale when variables exhibit all the properties of an interval scale along with an absolute zero value, making it the highest level of measurement among the four hierarchical levels, which include nominal, ordinal, and interval scales.
3.3.1.2 Descriptive analysis for variables with nominal scale
Nominal data, derived from the word meaning "name," refers to variables that can be categorized into distinct, mutually exclusive groups without any meaningful order This type of data is the least precise and complex among measurement scales, as it only allows for labeling without rank, equal spacing, or a true zero value Known as the nominal or categorical variable scale, it is primarily used for classification purposes, where associated numbers serve merely as tags rather than quantitative values In statistical software like SPSS, nominal data can be represented as either string alphanumeric or numeric, necessitating proper designation based on the variable's nature.
3.5.2.1 Overview of Logistic Regression Analysis
Regression analysis is a predictive modeling technique that examines the relationship between a dependent variable (often referred to as the "Y" variable) and one or more independent variables (the "X" variable) When multiple independent variables are utilized to predict or explain the dependent variable's outcome, this approach is called multiple regression.
Regression analysis can be broadly classified into two types: Linear regression and logistic regression In this study, the author goes ahead of logistic regression[70]
Logistic regression is a powerful classification algorithm utilized for predicting binary outcomes based on various independent variables In this context, a binary outcome refers to situations with only two possible results: the event either occurs (1) or it does not (0).
(0) Independent variables are those variables or factors which may influence the outcome (or dependent variable)[70]
Logistic regression is a statistical method used to estimate the probability of a binary event, facilitating effective classification of outcomes By predicting "yes" or "no" results, it empowers data analysts and businesses to make informed decisions, ultimately reducing the risk of loss and optimizing expenditures to enhance profitability.
Logistic regression offers several advantages, making it a preferred choice in machine learning Firstly, it is easier to implement compared to other methods Secondly, it performs effectively when the dataset is linearly separable Lastly, logistic regression yields valuable insights into the data.
In this study, logistic regression analysis is conducted on the basis of the equation owned by F-score model, as follows:
1 − 𝐹𝑟𝑎𝑢𝑑 = β0+β1ROA+β2FINST+β3LEV+β4INST+β5CG+β6CR+β7BDOUBT+ β8REC+β9AUCHANGE+β10BIG+β11TATA+β12DCHANGE+β13CEOPIC +ε
Or if it is demoted to:
E = Base on Natural Logarithm values β0 = Constant regression coefficient β1, β2, β3, β4, β5, β6, β7, β8, β9, β10,β11,β12,β13 = Regression coefficient of each variable
BIG = Quality of external auditor
In order to understand more how this equation operates and shows results, several tests are indicated and informed in details as follows:
3.3.2.2 The goodness of Fit Test
The goodness-of-fit test is a statistical hypothesis test used to determine how well sample data aligns with a normal distribution from a population This test assesses whether the sample data accurately represents the expected data in the actual population or if it is distorted By measuring the discrepancy between observed values and those predicted by a normal distribution model, the goodness-of-fit test evaluates the validity of the sample data.
Various methods exist for assessing goodness-of-fit in statistics, with some of the most widely used being the chi-square test, the Kolmogorov-Smirnov test, the Anderson-Darling test, and the Shapiro-Wilk test.
Goodness-of-fit tests are essential statistical methods used to assess the relationship between observed and predicted values in a model By evaluating how closely actual values align with predictions, these tests play a crucial role in decision-making, enabling the prediction of future trends and patterns.
The chi-square test is the most widely used goodness-of-fit test, primarily designed for discrete distributions This test is applicable only to data organized into classes or bins and necessitates a sufficiently large sample size to ensure accurate results.
The chi-square test, or chi-square test for independence, is a statistical method used to assess the validity of a hypothesis regarding a population derived from a random sample While it helps determine whether a relationship exists, it does not provide information on the nature or strength of that relationship, such as whether it is positive or negative.
The author employs the likelihood-ratio test to evaluate the overall fit of statistical models This test compares the goodness of fit between two competing models by examining the ratio of their likelihoods—one derived from maximizing the entire parameter space and the other from a constrained version If the observed data supports the null hypothesis, the likelihoods should only differ due to sampling error Consequently, the likelihood-ratio test determines if this ratio significantly deviates from one, or, in other terms, if its natural logarithm is significantly different from zero.
Nagelkerke's R² is an enhanced version of the Cox & Snell R-square, designed to provide a statistic that ranges from 0 to 1 While Cox and Snell's R² is derived from comparing the log likelihood of a model to that of a baseline model, it is limited by a theoretical maximum value of less than 1 when applied to categorical outcomes, even in ideal scenarios.
OPERATIVE DEFINITION OF VARIABLES AND MEASURES
In statistical research, a variable represents an attribute of the object being studied, making the selection of which variables to measure crucial for effective experimental design Experiments typically aim to determine the impact of one variable on another, such as the effect of salt addition on plant growth The independent variable is manipulated, while the dependent variables are measured to assess the outcomes of this manipulation Understanding these two types of variables is essential for accurate analysis and interpretation of research findings.
In an experiment, the dependent variable is manipulated to observe its effect on the outcome, as it relies on other measured factors This variable is anticipated to change due to the experimental manipulation of the independent variable(s), representing the presumed effect In this study, financial statement fraud, indicated by the F-score, is identified as the dependent variable, reflecting instances of fraudulent financial statements.
The F-score model is based on two key components found in audited financial reports: accrual quality and financial performance Accrual quality is measured using RSST accrual, which serves as an important proxy in evaluating the financial health of a company.
𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐓𝐨𝐭𝐚𝐥 𝐀𝐬𝐬𝐞𝐭𝐬 Every numerator and denominator will be described below:
WC (Working Capital): (Current Assets – Current Liability)
NCO (Non Current Operating Accrual): (Total Assets – Current Assets –
Investment and Advances) – (Total Liabilities – Current Liabilities - Long Terms Debt)
FIN (Financial Acrual): (Total Investment – Total Liablities)
Average Total Assets: (Beginning total assets – Ending total assets)/2
The second component of F-score formula is Financial Performance, which is measured on accounts receivable, changes on inventory accounts, changes on cash sales account, changes on earnings as follows[19]:
Financial Performance = Change on receivable + Change on inventories + Change on cash sales + Change on earnings
Independent variables are the factors that determine the outcome of an experiment and remain stable, unaffected by other variables being measured These variables represent the conditions systematically manipulated by the investigator and are considered the presumed causes of the results In this study, the independent variables are derived from the five components of the pentagon fraud theory: pressure, opportunity, rationalization, capability, and arrogance, which are detailed in the accompanying table.
Dummy variable, code 1 if restatement of audited financial statement, otherwise, code 0 Nominal
AUCHANGE = If there is a change in auditor, code 1 is given, otherwise if there is no change, it is coded 0.
BIG = Dummy variable coded 1 if the firm is audited by an auditor at leastbelonging to the "BIG", 0 otherwise
DCHANGE = If there is a change in Director, code 1 is given, otherwise if there is no change, it is coded 0.
DATA COLLECTION
This study aims to elucidate the impact of the pentagon fraud theory characteristics on fraudulent financial reporting Utilizing a quantitative research method, the author collects secondary data from annually audited financial statements and reports available on the Ho Chi Minh Stock Exchange's official website (https://www.hsx.vn/) The analysis employs descriptive statistical techniques and logistic regression to examine the data The research focuses on banking and financial sector companies listed on the Ho Chi Minh Stock Exchange (HSX) over a five-year period from 2016.
2020 with purposive sampling technique as sampling method The criterias are used to opt out wanted samples are as follows:
Qualified company number Companies went public and listed on Ho Chi Minh
Companies classified in the banking and financial sector in a row during the period 2016 - 2020 33
Table 9 Description of dependent variable and independent variables
CEOPIC = The frequent number of CEO picture is measured by total CEO photo that is displayed in an annual report
Companies published audited annual report in the period of 2016 - 2020 15 18
Companies were not delisted during the period
Data related to need-to-discuss variables are completely available ( data were entirely available on the pulication during the period 2016 -2020) 15 18
On the basis of above determinant criterias, the author singles out 18 companies and banks with 90 samples respectively
The author focuses on the banking and financial sectors due to their high susceptibility to fraudulent financial reporting In Vietnam, numerous scandals in these industries have significantly affected not only the companies involved and their employees but also external parties such as suppliers, government entities, and investors.
So detecting and preventing fraudulent financial reporting in a previous way is mostly able to maximize the unpredictable economic damages
The author built a data collection cycle of 8 stages, which include generation, collection, processing, storage, management, analysis, visualization, and finally interpretation Their explanation becomes more specific as follows:
This study utilizes secondary data sourced from the official website of the Ho Chi Minh Stock Exchange In cases where data is lacking, the author supplements information with official resources from company homepages or Vietstock Finance.
The author uses the manner of seeking information on annually audited inancial statements or annual reports ahead of samples regarding preceding optional variables
After data collection, the next crucial step is data processing, which encompasses several activities One key aspect is data wrangling, where raw data is cleaned and transformed into a more accessible and usable format This process is often referred to as data cleaning, data munging, or data remediation.
Once data is collected and processed, it is essential to store it for future reference, typically through the creation of databases or datasets In this case, the data is specifically saved in Excel on the author's desktop.
Data management, or database management, is the continuous process of organizing, storing, and retrieving data throughout the lifecycle of a project This comprehensive approach encompasses various aspects such as data storage, encryption, and the implementation of access logs and changelogs, which monitor who accesses the data and any modifications made.
Data analysis refers to processes that attempt to glean meaningful insights from raw data The author use different tools and strategies to conduct these analyses
To be more specific, the author uses SPSS software under version 20 to deal with the secondary data to produce desireable data [76]
Data visualization is the practice of transforming information into graphical formats using various visualization tools, which facilitates clearer communication of analysis to a broader audience While the author primarily utilizes numerical tables, the effective use of visual representations enhances understanding and engagement with the data.
The interpretation phase of the data life cycle allows for a comprehensive understanding of the author's analysis and visualizations Specifically, the author employs a robust interpretation process grounded in numerical output tables generated by SPSS software.
SPSS 20 software is used as a popular tool of processing data in previous researches After getting necessary secondary data transferred to Excel 2016 from purposive sampling technique, the author started conducting a data analysis on SPSS 20 software by uploading the whole data on the working screen of this software
The author employs the F-score model in conjunction with Logistic Regression analysis to evaluate the effectiveness of the model Key tests conducted include the Goodness of Fit Test, Overall Model Fit, and Nagelkerke R Square, which are essential for determining the model's acceptance and the suitability of independent variables This analysis is a crucial step in the hypothesis testing process.
In Chapter 3, the author discusses quantitative research as a data collection method, emphasizing the selection of the F-score model to measure the risk level of fraudulent financial reporting The chapter reconciles the dependent variable, Fraudulent Financial Reporting, with independent variables such as ROA, Finst, Lev, Inst, CG, CR, BDOUBT, REC, AUCHANGE, TATA, BIG, DCHANGE, and CEOPIC to effectively integrate with the F-score model Additionally, the author introduces logistic regression analysis, detailing the tests employed in this data analysis approach Finally, the chapter outlines the data collection process, including the research design, research process, and data processing methodologies.
From here, the results of data processing are disclosed in the next Chapter: Results and analysis.