Keywords
fraud theories, corporate fraud, network analysis
This article is included in the HEAL1000 gateway.
fraud theories, corporate fraud, network analysis
In recent literature, accounting fraud is associated with sustainable and ethical management. Fraud scandals are widespread in the global economy. Fraudulent financial statements represent unethical management and harm the long-term profitability of a company and its shareholders. Sustainability and transparency are fundamental concerns for businesses. Transparency is an indispensable quality in the business world. According to agency theory, conflicts between managers and their stakeholders lead to fraud. As a result of inadequate corporate governance, managers still commit fraud today. By using fraud theories to interpret human behaviour, we combine ethical and sustainability concerns in our research. Existing accounting literature explains fraud theories and attempts to quantify and analyse human behaviour. Human behaviour is an essential factor in fraud theories and connects inseparably with fraudulent financial statements, as the preparators of financial statements are humans. Consequently, if they have the factors described in fraud theories, they can lead a company more easily to fraud. In addition, fraud theories attempt to explain why managers commit fraud. What is the motive for committing fraud? What is the impact of financial statement fraud on the economy?
The field of financial statement fraud continues to evolve, and extensive theoretical research is needed to develop future research topics using trend analysis. There are numerous articles in the literature that focus on the analysis of financial statement fraud and corporate fraud. For example, Hogan et al. (2008) examined the fraud triangle based on related articles on financial reporting fraud and showed the characteristics of a fraudulent company and the function of an auditor’s opinion in detecting and preventing fraud. Trompeter et al. (2013) extend the study of Hogan et al. (2008) earlier. They focus on administration, ethics, criminology, and psychology.
In addition, Amiram et al. (2018) discuss the findings and trends in the accounting fraud literature. They describe in more detail the various findings and methods found in the fraud literature. In addition, Uysal (2010) examined business ethics research articles using a bibliometric analysis. In addition, Uysal (2010) determined the citation frequency of business ethics-related articles and used a co-citation analysis to determine the communication patterns in this research area. Accounting literature researchers examine and synthesize numerous models, qualitative, quantitative, and textual analyzes to determine how accounting fraud can be detected. On the other hand, there are a small number of researchers who examine future issues and trends in accounting fraud research.
In a comprehensive literature review, the research contributes to the study of accounting fraud theories and indicates the trends in the literature. These are the research questions:
1. Do a company’s ethical issues conflict with its sustainability issues? is the first research question.
2. How can fraud be detected by studying human behaviour using fraud principles? is the second research question.
3. What methods in the accounting literature are associated with theories of human behaviour and fraud? is the third research question.
Fraud theories
In the global economy, the phenomenon of accounting fraud and corporate fraud can be observed. Numerous fraud theories explain fraud as a factor of human behaviour. This paper examines fraud in light of the prevailing fraud theories.
Cressey examined the first theory of fraud (1953), where Cressy establishes a link between fraud and human behaviour. The ‘fraud triangle theory’ has been established as Cressey’s (1953) model in which three conditions that can lead to fraud are analysed; Opportunity, pressure, and rationalization. Fraud is essentially motivated by external forces. The motivation for managers to commit fraud is ‘pressure’. Cressey (1953), Albrecht et al. (2006), Lister (2007), and Manurung and Hadian (2013) all support the notion that non-financial and financial pressures motivate fraud by managers. In addition, according to SAS No. 99 (Ramos and West, 2003), external pressure, financial goals, personal financial need, and financial stability are motives for fraud by managers. According to Cressey (1953), Wilson (2018), Wolfe and Hermanson (2004), and Kelly and Hartley (2010), a fraudster’s opportunity lies in his or her capabilities. The fraudster can identify the structural weaknesses of the company and then break the trust. Inadequate corporate governance, a lack of internal control, and a lack of control by responsible personnel are conditions for opportunity development. According to SAS No. 99, there are three categories of accounting fraud, which are distinguished by organizational structure, sector and inadequate supervision. The final component of the fraud triangle is rationalization. Rationalization is an attitude based on a set of moral beliefs. The actions of fraudsters are justified by their beliefs. In summary, Cressey (1953) stated that the three components of pressure-chance-rationalization constitute the behavioural characteristic of a cheater.
Wolfe and Hermanson (2004) added a new component, ‘capability’, to the fraud triangle theory. Wolfe and Hermanson (2004) assumed that ‘capability’ refers to the personal skills and characteristics that lead administrators to commit fraud. The new fraud theory is known as the ‘Diamond Fraud’ theory. According to Wolfe and Hermanson (2004), the fraud diamond theory suggests that ‘opportunity’ opens the door to fraud. Motive and justification determine the individual who commits fraud. Therefore, the individual must have the ‘capability’ to recognize and see the ‘opportunity’ to commit fraud.
Furthermore, Crowe (2011) extends the fraud triangle to include ‘arrogance’ and ‘competence’. The new fraud theory is referred to as the ‘Fraud Pentagon Theory’ and consists of five elements: ‘Opportunity - Pressure - Rationalization - Competence – Arrogance’. Crowe’s (2011) theory extends Wolfe and Hermanson (2004) by adding two new components ‘competence’ and ‘arrogance.’ More specifically a person’s ‘competence’ is his or her measure to perform a capability, in this cause, fraud. ‘Arrogance’, on the other hand, is a human behavioural trait that dominates an organization and overrides internal rules. This person is able to develop fraud strategies and has the goal of enriching themselves and winning.
Voussinas developed the latest fraud theory (2019). This fraud theory is known as the ‘Fraud Hexagon Theory’ and consists of the following six components: stimulus, opportunity, rationalization, capability, collusion, and ego. According to Vousinas (2019), the explanation of these components is as follows: ‘Stimulus’ is the incentive for a leader to commit fraud; ‘Capability’ is the ability required to commit fraud; ‘Opportunity’ is the opportunity to commit fraud; ‘Ego’ is a characteristic of human behaviour; and ‘Collusion’ is the agreement of people to commit fraud and justify their actions, which is ‘rationalization’.
We searched for documents with the fraud theories within as keywords in article titles, abstracts, and keywords. We perform a keyword search for ‘Fraud Triangle’, OR ‘Fraud Diamond’ OR ‘Fraud Pentagon’, OR’ Fraud Hexagon’ as each of the key theories previously discussed. Also, we search all open access types available to the Scopus database and all citation information. In addition, we select bibliographical information as affiliations, serial identifiers, publishers, editors, and some other information as tradenames and manufacturers, conference information, including references and accession numbers. Also, in this research, we include all authors who have examined fraud theories and all the subject areas, for example, ‘business management and accounting and ‘social sciences’.
Furthermore, we include all document types like articles, conference papers, reviews, book chapters, conference reviews, notes and data papers. Also, we include documents in the final publication stage and articles in the press. We include countries such as the United States, Indonesia, Malaysia, Australia, United Kingdom, Canada, China, Ghana, France and Germany. The investigation was limited to papers published only in English from 2004 to 2022. We did not include in our research the current year 2023, as documents are still being published. The last research was done at the end of January. The results from the above criteria are to collect 302 papers.
In continuing for each document of 302 papers, we use the author’s keywords of each paper and the abstracts. Then we export the data in the CSV file and use the VOS viewer program for our bibliometric analysis.
After importing the CVS file into the VOS viewer program, we choose the type of analysis and counting method. From this section, we analyse all the keywords of selected articles. The results of selected keywords appeared in Table 1. In this research, we created a network synthesis of keywords from 302 published articles. In creating the network, we only summarized the keywords that the researchers indicated occurred more than twice in the articles. Since authors sometimes use different words with similar meanings, we removed keywords with synonymous meanings. In more detail, keywords with synonymous meanings were excluded, such as ‘internal control’ and ‘internal controls’, ‘financial statement fraud’ and ‘fraudulent financial statements’, as shown in Table 1. Then a word cloud was created with the most frequent keywords from 2004 to 2022, as shown in Figure 1.
Then we classify the keywords into clusters as shown in Table 5. A group of keywords is called a cluster and is included in the map. Clusters are not overlapping in the VOS viewer program. Clusters do not necessarily fully cover all keywords on a map. Consequently, there may be keywords that do not fit into any cluster. Thus, a keyword may fit into only one cluster. Clusters are identified by cluster numbers.
Furthermore, we create a map based on text data. More specifically, we select all abstracts of the 302 papers related to fraud theories, and we use the binary country method to choose the threshold. We use the option to create a term co-occurrence map based on text data. Then we choose the field of abstracts and the most frequent words in analysing abstracts. The results are shown in Table 6. Also, we create clusters in the VOS viewer program as shown in Table 7.
We use multidimensional scaling to create the bibliometric map to increase the relatedness and similarity of elements (keywords) in a low-dimensional space. According to Van Eck et al. (2006) and Van Eck and Waltman (2007), the association strength for similarities (sij) is calculated by equation 1.
For every set of keywords (item) i and j, VOS involves as input a similarity sij (sij ≥ 0). The similarities sij as weights calculated on a ratio scale. The locales of keywords (items) in a map minimizing by equation (2).
The purpose is the weighted sum to reduce the sum of the squares’ distance among every set of keywords (items). The weighted similarity among items is the squared distance between a pair of items. The average distance between two items equals one; to avoid solutions, all items have the exact location—the objective function given by equation 4, which is the ideal location of an item. The equation for the ideal location is shown in equation 4.
The ideal position of item i is defined as the weighted average of the positions of all other items. The ideal position of a keyword should occupy the most natural position that an item can have. Thus, the items appear to have the most desirable position that is closest to the ideal position described in Equation 2. The constraint in Equation 3 assumes that the positions of all keywords except the keyword (item i) are static and ignored. Equation 4 also describes the minimization of the objective function.
If the positions of all keywords i are static and the constraint is ignored, the minimization of the objective function has the effect of placing keyword i at its best position. Since the keywords have no static position and the solution is defined by the objective function and the constraint, we assume that the elements are not in the ideal position. So, according to the objective function, the keywords (elements) are placed no less than at the ideal position.
So, a map was created based on the counting technique and co-occurrence analysis with all keywords from 302 articles on fraud theories. The counting technique and co-occurrence analysis are used to determine the threshold value. The number of keywords to be selected is 48, and the lowest number of keywords is five out of the 988 keywords, 48 is the threshold. For each of the 48 keywords, the total strength of co-occurrence links with other keywords is estimated. The keywords with the largest total link strength are selected and listed in Table 4 below.
Metrics and methods of human behaviour
External pressures arise when managers want to respond to third-party demands. Managers may increase debt to external sources to remain competitive under these conditions. Another measure of external pressure in the fraud accounting literature is leverage and the ratio of total liabilities to total assets. Tiffani and Marfuah (2015), Manurung and Hardika (2015), and Skousen et al. (2009) suggest that the total debt/total assets ratio has a positive impact on financial statement falsification. Aprilia (2017) supports that a high debt to total assets ratio of a company can put pressure on the management team. When the company’s debt level is high, managers feel threatened by bankruptcy, which leads to pressure on management. External competitive pressure can cause managers to increase the company’s debt in order to stay in the market. Pressure can be referred to as another fraud factor.
The pressure/stimulus metrics for human behaviour created by Supri et al. (2018) and Sunardi and Amin (2018) provide evidence that financial goals can pressure management to achieve financial goals. In general, financial stability is a key issue for management. When financial stability is affected by economic conditions, managers feel pressured, which can lead to falsified financial statements. In the literature on accounting fraud, financial instability is measured by differences in total assets. If the differences in total assets are frequent in each year, it means that the company is in an unstable situation. Tiffani and Marfuah (2015), Manurung and Hardika (2015), and Skousen et al. (2009) also associate financial stability with falsification in financial reporting.
Personal financial needs are another human behavioural factor that can lead a company’s management to commit fraud. Tiffani and Marfuah (2015) pointed out that when the role of management is unclear, such as when the manager becomes part of the shareholders or participates in the board of directors’ commission, personal financial needs increase and may influence financial reporting. The ratio that can be used to measure own financial needs is the sum of shares held by insiders/total ordinary shares outstanding.
Return on equity (ROE) has been shown by Summers and Sweeney (1998), Brazel et al. (2006), Okoye et al. (2009), and Dechow et al. (2011) that it affects the percentage of fraud. A lower ROE means that the company has not attracted the attention of investors. In other words, the company management can edit the financial reports to attract more investors.
Setiawati and Baningrum (2018) found that companies with negative return on assets (ROA) tend to have poor economic performance. In another study, Emalia et al. (2020) supported that negative ROA does not meet financial objectives and that a company’s management team can manage financial reporting to improve the company’s financial performance. The human behaviour capability studied by Beasley (1996) supports that external board members lead to more effective management oversight and serve as a fraud prevention measure. Albrecht et al. (2010) also confirmed Beasley’s (1996) statement.
According to the literature, the last pressure factor is when a company seems to have liquidity problems. Persons (1995) proved that low current ratio leads to lower liquidity of a company’s assets and consequently a company is more vulnerable to short-term solvency.
According to fraud theories, capability is a component that can lead to fraud. Wolfe and Hermanson (2004) and Manurung and Hardika (2015) show that board changes can be a temporary period that causes stress and can lead to fraud. In addition, effective monitoring is one way for organisations to reduce the likelihood of fraud. Beasly et al. (2010) support that an audit committee with independent members can reduce the likelihood of fraud. Tiffani and Marfuah (2015) also suggest that an effective monitoring mechanism can reduce the risk of fraud. The measure of effective monitoring is the percentage of independent committee members.
Skousen et al. (2009) and Miller et al. (2012) investigated the possibility of human behaviour and demonstrated that claims have high inherent risk and can be manipulated. Their research was along the lines of Loebbecke et al. (1989), who reached the same conclusion. Thus, a high accounts receivable ratio may indicate manipulation in financial statements. Moreover, as described above, effective monitoring may measure the opportunity component of fraud theories.
Sykes and Matza (1957) suggested that a firm’s violation of internal regulations can be justified by rationalisation techniques. According to Skousen et al. (2009), rationalisation can be a measure when the auditor’s opinion affects the appearance of financial reporting. When the auditor’s opinion on the presentation of financial reporting includes the comment that it is not fair because it does not follow Generally Accepted Accounting Principles (GAAP), the management team tends to justify it (Lokanan and Sharma, 2018).
Another characteristic of human behaviour is integrity. Integrity can be measured by earnings management, sales history, and earnings growth. Revenue management occurs when management fails to meet financial goals. It is then possible to manipulate accounting principles (GAAP) to produce financial reports. Also, managers can exploit the flexibility of accounting rules and manipulate the level of earnings (Manurung and Hadian, 2013). In addition, integrity can be measured by sales performance. Assets support sales, so an increase in sales leads to an increase in assets (Brigham and Houston, 2006). If the sales growth ratio is positive, the company is viable. The sales growth ratio is also indicated as the market share or position of the company in the market. There are many ways for managers to manipulate sales, and sales growth ratio has increased in fraudulent financial reporting. Another measure of integrity is the earnings growth rate - usually measured by ROE (return on investment) or earnings per share. Generally, the profit growth rate measures the manager’s valuation. Managers can affect the cost of goods and sales sold, interest and operating expenses, and income tax. According to agency theory and the various conflicts, managers can manipulate all of the above ratios and present a company’s financial performance differently.
The metric used in the accounting literature for ego is how many images and information of the CEO are related to the company’s performance. According to Apriliana and Agustina (2017), the CEO applies the internal rules of a company. Thus, if a CEO is characterized by arrogance, this may act as a fraud factor.
Collusion is described in the Hexagon Theory and supports the link between fraudulent financial reporting and collusion. Collusion occurs when more than two individuals engage in deceptive practices to achieve a goal for their benefit - the members’ agreement involves money or other benefits to facilitate their work. The law opposes collusion because it is only for personal gain. When collusion exists, the likelihood of fraud increases. The main characteristics of collusion are, first, payments to management or employees to gain money or goods, and second, the presence of an intermediary for the acquisition of services and goods.
Table 3 summarizes all of the above measures used by researchers to detect fraud according to fraud theories. Many researchers have used regression models to examine the relationship between human behavioural characteristics and fraud theories.
We therefore captured 302 papers. Figure 1 shows the main keywords used in the search for fraud theories. Figure 1 shows the most frequent fraud theories authors have examined, and we help us determine the literature gap among fraud theories. Also, Figure 1 shows the relation or connection between fraud theories and the characteristics of human behaviour. In our bibliometric analysis, we use it as an additional result to answer the second research question.
In addition, fraud theories have attracted the attention of the research community over the years. As Publications on fraud theories from 2004 to 2022 are shown in Figure 2. Figure 2 shows all the publications contained in the sample of 302 articles as described in the methods section. We have not excluded any publication.
Table 2 also shows the journals with the most publications from 2004 to 2022 on fraud theories. According to the interest in fraud theories, the journals have become more specific. According to the Scopus database, the journals that have published topics related to fraud theories and human behaviour are shown in Table 2 below. The criterion of the selection of this journal is the number of articles which have been published from the period 2004 to 2022.
Skousen et al. (2009) examined the components of the fraud triangle based on SAS NO 99. Their research used a sample of fraud companies and matched them with non-fraud companies. Skousen et al. (2009) developed a broad range of variables as proxies of the components of the fraud triangle. They used the Univariate analysis to determine eight variables for the component of pressure and five variables for the component of opportunity. According to their results, change in assets, cash from operations -average capital expenditure/current assets, net cash flow from operating activities- cash dividends-capital expenditure are significant variables for external pressure. The variable of the percentage of ownership is significant for the component of personal financial need. For the component of opportunity, the nature of the industry and a dummy variable of CEO when simultaneously CEO hold, and managerial position are the most significant variables for the measurement of opportunity. Their results appear to improve over Persons (1995) to predict fraud substantially. The weakness of this research is the inability to explore significant variables for the components of rationalization.
Also, Manurung and Hardika (2015) studied empirical evidence for the components of the fraud triangle. According to their results, the most significant variables for detecting fraudulent financial statements are asset growth (for financial stability), ROA, the measurement of financial target and the leverage ratio as a measurement of external pressure. The study of Manurung and Hardika (2015) is consistent with Skousen et al. (2009).
On the other hand, Khamainy et al. (2022), examined the fraud diamond theory. Khamainy et al., the nature of industry and sales history significantly affect fraudulent financial statements. So, a higher increase in receivables and sales and the number of management-owned shares are suspicious ratios to investigating fraudulent financial statements. Effective monitoring negatively and significantly impacts fraudulent financial statements for the opportunity component. This result indicates that more members as independents can lead to more unbiased financial statements. Ratios of changes in total assets (a measure of financial stability), total debt/shareholder equity (external pressure), ROA (financial targets), earnings management (personal integrity) and changes of directors (capability) have no significant effect on the prediction of fraudulent financial statements. Khamainy et al. (2022), suggest further research to combine and compare more sectors as their research investigates only manufacturing firms.
Okoye et al. (2009) examined the impact factor of the fraud triangle in the audit procedure. More specifically, Okoye et al. (2009) examined an audit plan as a framework for audit procedure. Their results are consistent with the fraud triangle factors and prove the importance of personal integrity. In addition, Brazel et al. (2006) examined the relationship between non-financial and financial data and concluded that fraud companies have a higher percentage of revenue growth.
Zainudin and Hashim (2016) investigated financial ratios to identify fraudulent financial statements. The results of this research show that ratios such as total debt/total equity, total debt/total assets, net profit/ revenue, receivables/revenue, inventory/total assets, working capital/total assets, revenue/total assets and the logarithm of current assets/total assets are significant indicators in the research of fraudulent financial statements. The weakness of this study was the small sample and the hand-collected data, which limited this research.
In another survey, Akbar (2017) examined the Pentagon theory for Indonesian companies. He concluded that the factors of pressure, which are: ROA (financial target), changes in total assets (financial stability), leverage ratio (external pressure), and institutional ownership, are the most significant, which can lead to fraudulent financial statements. The results of this research are in the same line as those (Apprilia, 2017) and (Tiffani & Marfuah, 2015). In addition, Quraini and Rimawati (2018), examined the fraud pentagon theory and concluded that ROA (financial stability), ineffective monitoring (opportunity), changes in director and auditor, institutional ownership and CEO’s picture in the annual report did not affect fraudulent financial statements. Also, their research suggests continuing with a sample of the private sector, as their research sample was public government firms. The following table summarizes all the above.
Dependent variables | Definitions | Models | Authors | |
---|---|---|---|---|
Fraudulent financial statements (corporate fraud)-financial distress | Manipulation in financial reports (Characterize a firm to non-fraud or fraud) | F-score | Ratio | Dechow et al. (2011) |
M-score | Ratio | Beneish Model (1997) | ||
Z-Score | Ratio | Altman (1968) |
Independent variables | ||||
---|---|---|---|---|
Human Behaviours | Definitions | Metrics | Authors | |
Pressure/Stimulus | External pressure: | Leverage= total debt/total equity | Ratio | Manurung and Hardika (2015), Subramanyam (2017), Sihombing and Rahardjo (2014) |
External pressure: | cash from operations -average capital expenditure/current assets, net cash flow from operating activities- cash dividends-capital expenditure | Ratio | Skousen et al. (2009), Persons (1995) | |
Financial stability | Change in Total Assets (TA) | Ratio | Khamainy et al. (2022), Manurung and Hardika (2015), Skousen et al. (2009) | |
Personal financial need | Total shares owned by insiders/Total of ordinary shares in circulation | Ratio | Khamainy et al. (2022), Skousen et al. (2009) | |
Low interest attention from investors | ROE=net income/shareholder equity | Ratio | Dechow et al. (2011), Okoye et al. (2009); Brazel et al. (2006), Summers and Sweeney (1998) | |
Financial target | ROA (returns on assets) | Ratio | Khamainy et al. (2022), Annisya et al. (2016), Manurung and Hardika (2015) | |
Liquidity ratio can show financial troubles | Current assets/current liabilities | Ratio | Zainudin and Hashim (2016) | |
Capability | The percentage of independent members of committee | % of independent commissioners | Nominal | Akbar (2017), Skousen et al. (2009) |
Changes of directors made by the firm | change in directors | Nominal | Khamainy et al. (2022), Sihombing and Rahardjo (2014), Wolfe and Hermanson (2004), Bonner (1998) | |
Opportunity | Nature of Industry | Account receivable ratio | Ratio | Skousen et al. (2009), Sihombing and Rahardjo (2014), Summers and Sweeney (1998) |
Effective monitoring | number of independent audit committee members/total number of audit committees- | Nominal | Khamainy et al. (2022), Tiffani and Marfuah (2015), Beasly et al. (2010) | |
Rationalization | Change in auditors | Audit opinion | Nominal | Akbar (2017) |
Changes in auditors carried out by firm | Change in auditors | Nominal | Sihombing and Rahardjo (2014), Bonner (1998) | |
Personal integrity | Earnings Management | Discretionary accruals | Ratio | Khamainy et al. (2022), Manurung and Hadian (2013) |
History of Sales | Sales growth | Ratio | Khamainy et al. (2022), Chotimah and Susilowibowo (2014), Brigham and Houston (2006) | |
Earnings growth | Operating Profit | Ratio | Khamainy et al. (2022), Mahaputra and Adnyana (2012) | |
Ego/Arrogance | Number of CEO photos which associate with the achievements of the company | Total picture of CEO in the yearly reports | Nominal | Quraini and Rimawati (2018), Devy (2017) |
Collusion | There is a cooperation or contract that has the possibility for fraud | Corporation with government project | Nominal | Achmad et al. (2022), Yusrianti et al. (2020) |
We use the counting method and co-occurrence analysis to answer the first two research questions, “Do ethical issues conflict with sustainability issues in a company?” In addition, the second research question is “How can fraud be detected by studying human behaviour using fraud theories?”
Total link strength indicates the number of articles in which the two keywords appear. The total link strength is a positive mathematical value. The larger this value, the stronger the link. According to our results, rationalization, opportunity, corruption, and pressure are the most important human characteristics that can lead to corporate fraud. Ethics also has a relatively high total link strength.
Then we classify the keywords into clusters as shown in Table 5. A group of keywords is called a cluster and is included in the map. Clusters are not overlapping in the VOS viewer program. Clusters do not necessarily fully cover all keywords on a map. Consequently, there may be keywords that do not fit into any cluster. Thus, a keyword may fit into only one cluster. Clusters are identified by cluster numbers.
Nodes denote the keywords of each research article in a network diagram. A link in a network is one of the links between nodes in Figure 1. Keywords are nodes and have different attributes. Weight (link, total link strength, and occurrence) are key attributes. When keywords have been assigned to clusters, cluster numbers are an instance of an attribute. Numeric values represent these attributes. Weight attributes are non-negative values. Thus, the weight of a keyword should indicate the importance of the keyword. A keyword with a higher weight is interpreted as more significant than a keyword with a lower weight. There are also two weighting attributes in cluster analysis: The overall link strength and the links attribute. For a given keyword, the total link strength and links attributes indicate the number of links of a keyword with other keywords and the total strength of links of a keyword with other keywords.
Thus, according to our results, human behaviour and ethics are associated with fraud. The column of link attribute shows the number of keyword links with fraud theories. The total link strength attribute shows the total strength of keyword links of fraud theories.
The lowest number of occurrences of a keyword is 10 out of the 5982 entries; 182 meet the threshold. A relevance score is estimated for each of the 182 items. Based on this score, the most relevant keywords are selected and presented in Table 6 below. The number of keywords to be selected is 109.
Using the same procedure as described above, we classify the abstracts into clusters as shown in Table 7 - a set of keywords from the abstracts form clusters in the map. According to our results in Table 7, the keywords from the abstracts form four different clusters. The first cluster contains 13 keywords, “effect, ability, necessity, accounting fraud, board of directors, fraud diamond, financial target, financial stability, accounting fraud, effectiveness, external pressure, arrogance”, which are related to human behaviour and accounting fraud. It is also clear from the first cluster that financial goals and stability are key motivations for fraud. The second cluster establishes a link between the social impact of fraudulent financial statements and the prevention of fraud by auditors. The third cluster includes six keywords – ‘author, crime, corruption, cressey, financial fraud, fraud, behaviour’-and links human behaviour to corruption and crime. The last cluster contains only two terms - keywords: ‘employee fraud, asset misappropriation’ and connects asset misappropriation with employee fraud. So, from all the above clusters, we can conclude the relationship between human behaviour characteristic and fraudulent financial statements.
In this study, we used network analysis to examine whether ethical issues are at odds with issues of corporate sustainability; second, whether fraud is detected by examining human behaviour by linking fraud theories; and third, what methods are used in the accounting literature and are linked to human behaviour and fraud theories. We used the Scopus database from 2004 to 2022; our sample includes 302 articles. As part of this research, we created a network synthesis of keywords from 302 published articles. In creating the network, we summarized only the keywords provided by the authors that occurred more than twice in the articles. Then, we created a map based on the counting method and coincidence analysis of all keywords from 302 articles related to fraud theories. We use the counting method and co-occurrence analysis. Then, we create a map based on text data with the same sample of 302 articles on fraud theories and use the binary country method to choose the threshold.
Huge enterprises have more transactions than small and medium enterprises; consequently, accounting system control becomes more complex. Despite the convenience and usefulness of the accounting system, many transactions are easier to accounting errors and corporate fraud. Corporate fraud can cause a company to go bankrupt and lose the trust of investors and stakeholders. Companies therefore need to be critical and proactive to detect and prevent corporate fraud. Digital transformation has brought many changes to accounting, so fraud prevention has piqued the interest of managers and researchers.
Detecting corporate fraud was difficult with previous technologies because they could not fully account for the various factors of human behaviour and the potential risk of developing digital forensic technologies. Big data analytics and artificial intelligence play a critical and pivotal role in corporate fraud detection because they can manage massive amounts of data. Therefore, artificial intelligence, big data analytics, blockchain, and machine learning are welcomed by practitioners and researchers as they can effectively detect corporate fraud. Therefore, researchers are investigating the new technologies to combat corporate fraud. Li et al. (2020) and Fassia (2019) use blockchain technology and big data analytics to predict corporate fraud. Li et al. (2020) also supports that big data analytics can uncover accounting errors and detect fraud by auditors and companies because artificial intelligence can detect fictitious transactions faster than auditors. In addition, another part of the researchers uses machine learning to develop a new model to detect corporate fraud (Bao et al., 2020; Brown et al., 2020).
Our research attempts to fill the gap in the literature review because, according to Yu and Rha (2021), research investigating accounting fraud is increasing, but there is not enough research integrating practises and theories. This thesis contributes to the literature by examining human behaviour according to fraud theories and existing methods for detecting corporate fraud in the form of a traditional review. In this study, the most widely used fraud theories and the existing methods for detecting corporate fraud are explained.
A company’s financial distress can be a fraud factor because management wants to improve the company’s economic performance (Aviantara 2021). Thus, payables, expense, and revenue accounts can be manipulated (Utami and Pusparini, 2019). Future research needs to analyse more Pentagon and Hexagon fraud theories, which are more recent and have not yet been analysed in detail. Also, future research needs to analyse more of the human behaviour characteristics related to the Pentagon and Hexagon fraud theories.
UK Data Service: All Fraud Theories: A Systematic Review Approach, 2004-2022. https://doi.org/10.5255/UKDA-SN-856474 (Chimonaki, Papadakis, and Lemonakis, 2023).
The project contains the following underlying data:
• allfraudtheories.csv. (Final article details included in this study).
• Explanationofourworkflow.odt. (Flowchart of selection process used in this systematic review).
UK Data Service: PRISMA checklist and flow chart for ‘Perspectives in fraud theories – A systematic literature review approach’. https://doi.org/10.5255/UKDA-SN-856474 .
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
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Are the rationale for, and objectives of, the Systematic Review clearly stated?
Yes
Are sufficient details of the methods and analysis provided to allow replication by others?
Yes
Is the statistical analysis and its interpretation appropriate?
Yes
Are the conclusions drawn adequately supported by the results presented in the review?
Partly
If this is a Living Systematic Review, is the ‘living’ method appropriate and is the search schedule clearly defined and justified? (‘Living Systematic Review’ or a variation of this term should be included in the title.)
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Management, Accounting and Finance.
Are the rationale for, and objectives of, the Systematic Review clearly stated?
No
Are sufficient details of the methods and analysis provided to allow replication by others?
No
Is the statistical analysis and its interpretation appropriate?
Not applicable
Are the conclusions drawn adequately supported by the results presented in the review?
No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: accounting, fraud, auditing
Are the rationale for, and objectives of, the Systematic Review clearly stated?
Yes
Are sufficient details of the methods and analysis provided to allow replication by others?
Yes
Is the statistical analysis and its interpretation appropriate?
Yes
Are the conclusions drawn adequately supported by the results presented in the review?
Yes
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Management, Accounting
Alongside their report, reviewers assign a status to the article:
Invited Reviewers | |||
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1 | 2 | 3 | |
Version 1 07 Aug 23 |
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Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list:
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