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 paper investigates the relationship between risk management practices and potential fraudulent financial reporting in Malaysia by considering recent regulatory reforms of the Malaysian government on risk management practices.

The sample of this study was based on 257 firm-year observations during the 2012–2017 period. This study employed panel-least square regressions with period fixed effects.

This study found a significant association between risk management activities in the disclosure and potential fraudulent financial reporting. Nevertheless, this study found there is insignificant effect of the risk-management committee in reducing potential of fraudulent financial reporting.

This study is a pioneer research that relates firms’ risk management practices with potential fraudulent financial reporting measured by F-score. Thus, this study provides an insight to regulators on the extent of risk-management practices in deterring potential fraudulent financial reporting which can be used as an input for greater enforcement of risk-management regulations.

The purpose of this paper is to investigate and analyze the adoption of digital technologies in the banking industry and its impact on the rise of digital fraudulent activities, specifically focusing on online banking frauds. This paper aims to provide insights into the current technologies implemented by banks to secure their online banking systems and explores the methods used by cybercriminals to exploit security vulnerabilities in these systems.

In order to understand how digital technologies in banking can be secured against online fraud, this research conducted a systematic literature review (SLR) on digital banking, online banking fraud, and security measurements. The review encompasses a variety of sources from online databases such as Emerald Insight, Google Scholar, IEEE, JSTOR, Springer and Science Direct.

The key finding of the paper is that the adoption of digital technologies in the banking industry has led to a significant increase in digital fraudulent activities, particularly in the form of online banking frauds. This paper emphasizes that these frauds have become a global concern and have evolved into an industry where cybercriminals use sophisticated tools such as phishing attacks, denial-of-service attacks, Trojan horses, malware infections, identity theft and computer viruses.

This study relies solely on a literature review without incorporating primary data or case studies; therefore, it might miss out on the firsthand experiences and perspectives of banks and cybersecurity professionals.

This study emphasizes the need for banks to adopt advanced security measures to safeguard their online banking systems.

This study underscores the importance of ongoing training and awareness programs for both bank employees and customers.

This study specifically addresses the adoption of digital technologies in the banking industry and its correlation with the increase in digital fraudulent activities. This focus on the intersection of technology and fraud in the banking sector is a distinctive aspect. This study conducts a SLR to examine the current technologies implemented by banks to safeguard their online banking systems. This comprehensive approach provides insights into the diverse security measures used by banks to protect against various types of cyber threats.

This study aims to address the relationship between economic complexities (EC) and the green economy (GE) with fraud in the listed companies on the Tehran stock exchange. The authors study whether EC and GE increase the detection of financial statement fraud.

The authors used a multiple regression model based on the panel data method and fixed effect model to test hypotheses. The sample includes 1,351 companies listed on the Iranian stock exchange from 2014 to 2021.

The results show a negative and significant relationship between EC and GE with financial statement fraud.

Since this research is the first to address the mentioned topic in emerging markets, it provides helpful insights for financial statement users, analysts and legal entities. The study fills the literature gap and promotes knowledge regarding its relevant literature.

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.

Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience.

The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots.

The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions.

The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.

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.

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 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.

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.