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Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

This paper aims to investigate the perceptions of financial accountants and both internal and external auditors regarding the impact of corporate governance (CG) and information technology (IT) on the detection and prevention of fraud within organizations.

Primary data were collected from 250 financial accountants, internal auditors and external auditors through questionnaires. The non-probability snowball sampling technique was used for data collection, with the sample t-test, one-way ANOVA and paired sample t-test applied for analysis.

The results indicate that robust CG practices and IT techniques significantly aid in detecting and reducing fraudulent activities by minimizing opportunities, rationalizations, pressures and capabilities of potential employees to commit fraud. Internal controls also play a significant role in reducing instances of fraud. Notably, ethical officers and ethical training were not perceived as significantly effective in preventing and detecting fraud, leading to a perception that fraudulent practices are prevalent and increasing the risk of future fraudulent activities.

This study recommends the adoption of strong CG practices to identify potential fraud within an organization. Moreover, IT techniques should be tailored to specific needs for effective utilization. Furthermore, the government should increase awareness regarding data provision by departments, organizations and other related personnel. Future research could use secondary data from various regions to expand the literature in this field.

This research uniquely combines three significant factors: CG, IT and forensic accounting in fraud detection and prevention. It contributes to the enhancement of literature about fraud and its preventive and detective measures. The results of this study set the seed for future research, government policymaking and enhanced organizational practices.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

Using a qualitative grounded theory approach, this study explores the methods experienced external auditors use to detect fraudulent financial reporting (FFR) during standard audits.

Semi-structured interviews were conducted with 24 experienced external auditors to explore the methods they used to detect FFR successfully during standard external audits.

The authors find 58 methods used for FFR detection, out of which the following methods are frequently used and help in detecting more than one type of FFR: (1) specific analytical procedures, (2) positive confirmation, (3) understanding of the client's business and industry, (4) the inspection of specific documents, (5) a detailed analysis of the audit client's anti-fraud controls and (6) investigating tip-offs from suppliers, employees and customers.

Based on the grounded theory approach, the authors theorise that auditors must return to the basics and focus on specific audit procedures highlighted in this study for effective fraud detection.

The study provides practical guidance, including 58 methods used in audit practice to detect FFR. This knowledge can improve auditors' skills in detecting material misstatements due to fraud. Besides, analytical procedures and positive confirmation helped external auditors in this study detect all forms of FFR, yet they are overlooked in the external audit practice. Therefore, audit firms should emphasise the significance of these audit procedures in their professional audit training programmes. Audit regulators should advise auditors to consider positive confirmation instead of negative confirmation in financial audits to increase the likelihood of FFR detection. Moreover, audit standards (ISA 240 and SAS 99) should explicitly require auditors to conduct a detailed analysis of the client's anti-fraud controls.

This is the first study to identify actual, effective methods used by external auditors in detecting FFR during the ordinary course of an audit.

This paper aims to investigate the role of forensic accounting skills in enhancing auditor’s self-efficacy towards fraud detection in Egypt. Additionally, it explores the moderating effect of computer-assisted audit techniques and tools (CAATTs) application on the relationship between accounting and auditing skills and auditor’s self-efficacy, as well as its role in enhancing fraud detection.

A cross-sectional survey was developed and distributed to 117 external auditors working in Egypt. Partial least square structural equation modelling is used to examine the study hypotheses.

The results show a significant direct relationship between effective communication skills, psycho-social skills, accounting and auditing skills and an auditor’s self-efficacy. Additionally, the results show a significant direct relationship between auditor’s self-efficacy and fraud detection. It is revealed that CAATTs application moderate the relationship between auditor’s self-efficacy and fraud detection. In contrast, the results do not show a significant relationship between technical and analytical skills and auditor’s self-efficacy.

The originality of this research paper lies in its exploration of the role of forensic accounting skills in enhancing auditor’s self-efficacy towards fraud detection in Egypt. It sheds light on the role of improved auditor’s self-efficacy in detecting fraud. Additionally, this study further enhances the understanding of the potential benefits of using technological advancements in the audit process. It provides insights for accounting professionals and regulatory bodies in Egypt, highlighting the importance of leveraging forensic accounting skills and using CAATTs to enhance fraud detection efforts.

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 aims to reviews the literature on applying visualization techniques to detect credit card fraud (CCF) and suspicious money laundering transactions.

In surveying the literature on visual fraud detection in these two domains, this paper reviews: the current use of visualization techniques, the variations of visual analytics used and the challenges of these techniques.

The findings reveal how visual analytics is used to detect outliers in CCF detection and identify links to criminal networks in money laundering transactions. Graph methodology and unsupervised clustering analyses are the most dominant types of visual analytics used for CCF detection. In contrast, network and graph analytics are heavily used in identifying criminal relationships in money laundering transactions.

Some common challenges in using visualization techniques to identify fraudulent transactions in both domains relate to data complexity and fraudsters’ ability to evade monitoring mechanisms.

This study reviews and discusses the accounting literature that analyzes the role of auditors and enforcers in the context of fraud.

This literature review includes both qualitative and quantitative studies, based on the idea that the findings from different research paradigms can shed light on the complex interactions between different financial reporting controls. The authors use a mixed-methods research synthesis and select 64 accounting journal articles to analyze the main proxies for fraud, the stages of the fraud process under investigation and the roles played by auditors and enforcers.

The study highlights heterogeneity with respect to the terms and concepts used to capture the fraud phenomenon, a fragmentation in terms of the measures used in quantitative studies and a low level of detail in the fraud analysis. The review also shows a limited number of case studies and a lack of focus on the interaction and interplay between enforcers and auditors.

This study outlines directions for future accounting research on fraud.

The analysis underscores the need for the academic community, policymakers and practitioners to work together to prevent the destructive economic and social consequences of fraud in an increasingly complex and interconnected environment.

This study differs from previous literature reviews that focus on a single monitoring mechanism or deal with fraud in a broadly manner by discussing how the accounting literature addresses the roles and the complex interplay between enforcers and auditors in the context of accounting fraud.

This study aims to explore the relationship between fraud triangle theory (FTT) and the accounting fraud phenomenon in all listed companies in China.

The CSMAR database is used as the sample, including 16,063 data of all listed companies in Shanghai and Shenzhen markets for the 2010–2020 period. The authors also use quantitative methods, such as regression analysis, to investigate the relationship between five variables (cover three elements of FTT) and fraud occurrence.

Results show that leverage and liquidity ratios positively affect fraud detection, whereas return on net equity, audit size and independent director percentage negatively affect fraud detection.

This study enriches theoretical research on the causes of accounting fraud in China and is of great significance to the sound development of China’s capital market.