Search results

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

This study aims to observe people’s decisions to commit fraud. This study is important in the current time because it provides insights into the development of fraudulent intentions within individuals.

The information used in this study is derived from semi-structured interviews, conducted with 16 high-ranking officials who are employed in Indonesian local government positions.

The study does not have strong evidence to support prior studies assuming that situational factors or social enablers have direct effects on fraud intentions. As suggested, individual factors which are related to moral reasoning (moral judgment and rationalisation) emerge as a consequence of social enablers. The significant role of that moral reasoning is to rationalise any fraud attempt as permissible conduct. As such, when an individual is capable of legitimising his/her fraud attempt into appropriate self-judgement, s/he is more likely to engage in fraudulent behaviours.

This study offers practical prescriptions in guiding the management to develop strategies to curb fraudulent behaviours. The study suggests that moral cognitive reasoning is found to be a parameter of whether fraud is an acceptable option or not. So, an understanding of observers’ moral reasoning is helpful in predicting the likelihood of fraud within an organisation or in detecting it.

This study provides a different perspective on the psychological pathway to fraud. It becomes a complement work for the fraud triangle to explain fraudulent behaviours. Specifically, it provides crucial insights into the underlying motivations that lead individuals to accept invitations to engage in fraudulent activities.

According to the Association of Certified Fraud Examiners, financial statement fraud represents the smallest amount of fraud cases but results in the greatest monetary loss. The researcher previously investigated the characteristics of financial statement fraud and determined the presence of 16 fraud indicators. The purpose of this study is to establish whether investors and other stakeholders can detect and identify financial statement fraud using these characteristics in an analysis of a company’s annual report.

This study analyses a financial statement fraud case, using the same techniques that were previously applied, including horizontal, vertical and ratio analysis. These are preferred because stakeholders have relatively easy access to them.

The findings show several fraud characteristics, with a few additional ones not previously found prevalent. Financial statement fraud thus tends to differ between cases. It is also easier to detect and identify fraud indicators ex post facto.

This study is a practical case showing that financial statement fraud can be detected and identified in the financial statements of companies that commit fraud.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

A total of 25 years of research on the audit society has provided rich and engaging accounts of the ways in which rituals of verification have conditioned organizations and individuals to think and act. In contrast, this paper aims to explore the possibility of conditions through which things and spaces are enacted to be non-auditable.

Using the concept of proliferation and rarefaction (Callon and Law, 2005), the paper adopts a case-comparison design to explore two empirical sites. The first investigates a vast excess of audit structures against the case of the biggest corporate fraud in German accounting history, the Wirecard scandal. The second discusses the configuration of Tinder, the most popular provider of mobile dating and the absence of visible verification mechanisms.

The paper argues that things can become non-auditable through two mechanisms. Based on the two empirical sites, non-auditability can happen through an overload of auditable resources or, through the withdrawal of required resources. The paper discusses the consequences of this finding and suggests avenues for future research on non-auditability.

While accounting scholars have extensively addressed the audit explosion and traced how audit practices have journeyed into ever more novel terrains, this paper discusses forms of escape from the value-subverting and reductive accounts incorporated in the audit society. It thereby points to conditions under which accounting ends.

The phenomenon of corruption requires extra handling to achieve zero corruption. The purpose of this paper is to examine the integrated governance, risk management and compliance (GRC) implementation, the quality of internal audits and management's commitment to improving the ability to detect corruption and its impact on the company's financial performance.

This paper used primary and secondary data. Financial statement data and survey results from participants in 69 state-owned companies were analyzed using the Partial Least Square method.

There was a positive and significant effect of the integrated GRC implementation, quality of internal audit and management's commitment to increasing the organization's internal capability in detecting corruption. However, the failure to detect corruption mediates the effect of management commitment on financial performance. Besides, the organization's three internal factors could be better because their functions could be more optimal and require further improvement.

State-owned companies are continuing to be restructured, so these results can be helpful for now. However, they must update continuously with developments related to the composition and classification of state-owned companies.

Organizations can improve their ability to detect corruption in the workplace by using an early warning system such as the integrated GRC, internal audit quality and a high commitment from management.

To the author's limited knowledge, empirical research on integrated GRC implementation, internal audit quality and management commitment are still rare if they improve the detection of corruption ability. It uses the factors that cause corruption in the fraud hexagon to analyze the financial performance.

The aim of this study is to approach the way in which corporate governance influences the occurrence of financial fraud, as expressed by the M-Beneish score. In order to get further into the topic, we have first computed a corporate governance score based on the comply-explain statement and then selected a few elements that are part of the corporate governance reporting: equilibrium of board members (EQUIL), independence of board members (INDEP), selection of the board members (NOM), remuneration policy (REM), audit committee (AUDIT) and the proportion of female directors on boards (GenF). They were tested, one by one, using the financial fraud score to see the way in which they interact.

The study is conducted on a sample of 65 companies listed on the Bucharest Stock Exchange (BSE) for the 2016–2022 period. The data were processed using three-stage general least square [general least squares (GLS), with iteration, igls and option] with a common first-order panel-specific autocorrelation correction, so as to explain how a poor adoption of the corporate governance score and its elements has a negative implication for the M-Beneish score, controlling for the auditor opinion, type of auditing company and if the company is privately owned.

The results support most of our research hypothesis, revealing that a poor adoption of the corporate governance score and its components – AUDIT, EQUIL, INDEP and GenF – negatively influences the M-Beneish score, i.e. a low corporate governance score will lead to an increase in financial fraud. This is an encouraging aspect, for an improved adoption of the corporate governance principles reduces the occurrence of financial fraud.

This is a study that concerns the relationship between corporate governance and financial fraud for the case study for Romania.

The study highlights the importance of adopting the corporate governance code applied to the Romanian business environment. By measuring the presence of financial fraud appearance through the M-Beneish score, we have managed to outline the negative relationship between the two components. Thus, it is an important aspect of which companies should take account, so they will have long-term benefits and ensure the continuity of the business.

The policy implications of this project are for policymakers, so that they will understand how a good corporate governance mechanism will enhance high-performing businesses. Different aspects regarding corporate governance were validated and are in the process of being validated. Managers can extract and try to understand and apply the good characteristics of corporate governance for the well-being of their companies. At a broader level, the macroeconomic environment will increase its own well-being while encouraging market players to enhance qualitative corporate governance reporting. There is no doubt that corporate governance has a positive impact on businesses.

The study highlights the importance of adopting the corporate governance code as applied to the Romanian business environment. By measuring the occurrence of financial fraud using the M-Beneish score, we have managed to outline the negative relationship between the two components. Therefore, this is an important aspect that companies should take into account in order to have long-term benefits and ensure the continuity of their business.

This research study aims to identify barriers to incorporating forensic accounting into accounting curricula in Jordanian universities. The study examines the differences in perspectives among various accounting education stakeholders, including students, educators and accounting and auditing employees/managers, on forensic accounting education.

The research methodology is quantitative and involves administering a survey questionnaire. The data obtained are analyzed using techniques including t-test analysis, one-way ANOVA and post-hoc.

The study reveals that educators have a more favorable view toward incorporating forensic accounting into university accounting curricula in Jordan, while accountants and auditors (employees/managers) are more strongly convinced of its importance. Furthermore, the biggest challenge to integrating forensic accounting, as perceived by stakeholders, is the lack of related job opportunities.

The study significantly contributes to accounting education research by providing valuable information on barriers to incorporating forensic accounting into the accounting curricula of Jordanian educational institutions from the perceptive of various stakeholders. Therefore, this study may assist educators in overcoming obstacles in offering forensic accounting education.

The study carries important implications for the inclusion of forensic accounting in the accounting curricula of Jordanian educational institutions. By comprehending the different viewpoints of various stakeholders, educators and policymakers can address recognized challenges and strive for the effective integration of forensic accounting in accounting curricula. As a result, accounting students will receive a more comprehensive education, and graduates will be better equipped for successful careers in the field.

This study aims to determine experimentally factors affecting the satisfaction of retail stock investors with various investor protection regulatory measures implemented by the Government of India and Securities and Exchange Board of India (SEBI). Also, an effort has been made to gauge the level of satisfaction of retail equities investors with the laws and guidelines developed by the Indian Government and SEBI for their invested funds.

To accomplish the study’s goals, a well-structured questionnaire was created with the help of a literature review, and copies of it were filled by Punjabi retail equities investors with the aid of stockbrokers, i.e. intermediaries. Amritsar, Jalandhar, Ludhiana and Mohali-area intermediaries were chosen using a random selection procedure. Xerox copies of the questionnaire were given to the intermediaries, who were then asked to collect responses from their clients. Some intermediaries requested the researcher to sit in their offices to collect responses from their clients. Only 373 questionnaires out of 1,000 questionnaires that were provided had been received back. Only 328 copies were correctly filled by the equity investors. To conduct the analysis, 328 copies, which were fully completed, were used as data. The appropriate approaches, such as descriptives, factor analysis and ordinal regression analysis, were used to study the data.

With the aid of factor analysis, four factors have been identified that influence investors’ satisfaction with various investor protection regulatory measures implemented by government and SEBI regulations, including regulations addressing primary and secondary market dealings, rules for investor awareness and protection, rules to prevent company malpractices and laws for corporate governance and investor protection. The impact of these four components on investor satisfaction has been investigated using ordinal regression analysis. The pseudo-R-square statistics for the ordinal regression model demonstrated the model’s capacity for the explanation. The findings suggested that a significant amount of the overall satisfaction score about the various investor protection measures implemented by the government/SEBI has been explained by the regression model.

A study could be conducted to analyse the perspective of various stakeholders towards the disclosures made and norms followed by corporate houses. The current study may be expanded to cover the entire nation because it is only at the state level currently. It might be conceivable to examine how investments made in the retail capital market affect investors in rural areas. The influence of reforms on the functioning of stock markets could potentially be examined through another study. It could be possible to undertake a study on female investors’ knowledge about retail investment trends. The effect of digital stock trading could be examined in India. The effect of technological innovations on capital markets can be studied.

This research would be extremely useful to regulators in developing policies to protect retail equities investors. Investors are required to be safeguarded and protected to deal freely in the securities market, so they should be given more freedom in terms of investor protection measures. Stock exchanges should have the potential to bring about technological advancements in trading to protect investors from any kind of financial loss. Since the government has the power to create rules and regulations to strengthen investor protection. So, this research will be extremely useful to the government.

This work has societal ramifications. Because when adequate rules and regulations are in place to safeguard investors, they will be able to invest freely. Companies will use capital wisely and profitably. Companies should undertake tasks towards corporate social responsibility out of profits because corporate houses are part and parcel of society only.

Many investors may lack the necessary expertise to make sound financial judgments. They might not be aware of the entire risk-reward profile of various investment options. However, they must know various investor protection measures taken by the Government of India & Securities and Exchange Board of India (SEBI) to safeguard their interests. Investors must be well-informed on the precautions to take while dealing with market intermediaries, as well as in the stock market.

Blockchain’s potential is so significant that business activities across all industries can be drastically altered. Furthermore, the characteristics of blockchain appear to be well-suited to accounting requirements. However, accounting professionals’ attitude and intention toward blockchain adoption are not clear, particularly in India. Thus, this study aims to investigate and evaluate accountants’ intention to adopt blockchain technology in accounting activities.

This study examined and assessed accountants’ intention to use blockchain in accounting. To effectively measure usage intention, this study extended the unified theory of acceptance and use of technology (UTAUT) model by including context-specific constructs. To empirically test and validate the proposed model, data were collected from “369” professional accountants in India.

The findings revealed that facilitating conditions, performance expectancy and initial trust had a significant impact on adoption. Furthermore, the regulatory framework materially moderated the association between usage intention and its predictors.

These findings provide new empirical evidence about the impact of different predictors of usage intention by extending the UTAUT model. Relevant stakeholders can refer to this pioneering study to increase the adoption of blockchain as an efficient and trustworthy system among professional accountants, particularly in developing countries such as India.