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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 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.

The purpose of this study is to explore how the risk of management motives for fraud can be assessed in external audits.

Semi-structured interviews were conducted with 26 experienced external auditors to explore their perspectives on the methods they employ to assess the risk of management motives for fraud.

The study identifies six methods external auditors can use to assess management motives for fraud. It emphasises that assessing management motives requires auditors to go beyond understanding these motives and necessitates a sceptical and analytical mindset. Auditors need to identify the accounts most vulnerable to management manipulations, observe management attitudes and assess the credibility of management assertions. The auditors in this study highlight specific accounts frequently manipulated by management. Still, manual year-end journal entries are the most vulnerable to management manipulations as they are subject to fewer controls. They recommend increasing the sample size to 100% and assigning more experienced staff, particularly, those with qualifications in fraud examination or anti-fraud training, to audit these vulnerable accounts thoroughly. They also provided examples of how auditors can identify management motives for fraud, observe management attitudes and assess the credibility of management assertions.

Audit standards (e.g. ISA 240, SAS99) lack explicit guidance on assessing management motives for fraud, but auditors are required to consider it in fraud risk assessment. This study proposes guidance recommendations to improve auditors' ability to assess this risk, which could be integrated into professional audit standards and training materials to improve auditors' professional scepticism, ability to challenge management and skills in fraud risk assessment.

Assessing the risk of management motives for fraud in external audits has received limited attention in the literature. To the best of the authors’ knowledge, this study is the first to address this knowledge gap.

This study aims to examine the ability of big data analytics (BDA) to investigate financial reporting quality (FRQ), identify the knowledge base and conceptual structure of this research field and explore BDA techniques used over time.

This study uses a comprehensive bibliometric analysis approach (performance analysis and science mapping) using software packages, including Biblioshiny and VOSviewer. Multiple analyses are conducted, including authors, sources, keywords, co-citations, thematic evolution and trend topic analysis.

This study reveals that the intellectual structure of using BDA in investigating FRQ encompasses three clusters. These clusters include applying data mining to detect financial reporting fraud (FRF), using machine learning (ML) to examine FRQ and detecting earnings management as a measure of FRQ. Additionally, the results demonstrate that ML and DM algorithms are the most effective techniques for investigating FRQ by providing various prediction and detection models of FRF and EM. Moreover, BDA offers text mining techniques to detect managerial fraud in narrative reports. The findings indicate that artificial intelligence, deep learning and ML are currently trending methods and are expected to continue in the coming years.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive analysis of the current state of the use of BDA in investigating FRQ.

This study aims to examine how auditors perceive the influence of crucial fraud prevention factors in deterring financial statement fraud within the corporate sector. Additionally, this research explores the mediating effect of fraud awareness in elucidating the impact of ethical leadership and internal control systems on preventing financial statement fraud.

The study used an online survey, targeting a sample of 141 professionally qualified auditors with at least one year of practical experience in the field. The researchers used “Structural Equation Modeling (SEM)” to examine relationships between latent variables using partial least squares structural equation modeling. The study investigated the impact of whistleblowing systems, fraud awareness, ethical leadership, internal control systems and corporate governance on fraud prevention.

This research finding provides evidence to the corporate sector by establishing the significance of fraud awareness as the most influencing factor in preventing financial statement fraud. Furthermore, the combined explanatory variables account for 77.4% of the overall variance in financial statement fraud prevention. The study reveals a partial mediation effect of fraud awareness on the relationship between the internal control system and financial statement fraud prevention.

This research finding may assist in developing an effective fraud prevention programme to mitigate fraud instances and improve financial reporting quality. In the corporate sector, each organisation should clearly specify the policies on whistleblowing systems, fraud awareness training, internal control systems and corporate governance. To foster a comprehensive fraud prevention programme, the leaders should enforce these policies with employee support.

This research integrated crucial elements to develop a new theoretical framework for investigating financial statement fraud prevention within the corporate context. Accordingly, this research framework provides a more in-depth explanation of preventing financial statement fraud from an auditor’s perspective. Additionally, this research is the first to explore the mediating role of fraud awareness in influencing the effectiveness of the internal control system in preventing financial statement fraud.

This study aims to investigate the moderating role of natural language processing natural language processing (NLP) on the relationship between AI-empowered AIS (data gathering, data analysis, risk assessment, detection, prevention and Investigation) and auditing and fraud detection.

Quantitative methodology was adapted through a questionnaire. In total, 221 individuals represented the population of the study, and SPSS was used to screen primary data. The study indicated the acceptance of the hypothesis that “Artificial Intelligence in AIS has a statistically significant influence on auditing and fraud detection,” showing a strong correlation between auditing and fraud detection. The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection.

The study’s implications lie in its contribution to the development of theoretical models that explore the complementary attributes of AI and NLP in detecting financial fraud.

A cross-sectional design is a limitation.

NLP is a useful tool for developing more efficient methods for detecting fraudulent activities and audit risks.

The study’s originality stems from its focus on the use of AI-empowered AIS, a relatively new technology that has the potential to significantly impact auditing and fraud detection processes within the accounting field.

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.

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.

Although corporations exert considerable efforts to uphold ethical standards in their business operations, fraud instances persist as an enduring and formidable challenge within organisations, defying their utmost efforts. The presence of fraud poses a substantial and recurring threat to corporations, leading to significant financial losses on an annual basis. This emphasises the crucial need for a comprehensive understanding of the factors contributing to fraudulent activities and the intricate nature of fraud risk factors inherent in business operations. Therefore, this paper aims to enhance the efficacy of fraud detection and prevention measures through critical analysis and refinement of established fraud theories, drawing upon the existing literature on this subject matter.

This paper offers a comprehensive qualitative analysis of the existing literature, thoroughly reviewing prominent models that aim to elucidate the underlying motivations behind fraudulent behaviour. Moreover, drawing upon the existing theoretical foundation, this study conceptualises a model that enhances the understanding of the crucial factors contributing to fraudulent behaviour.

The study presents new theoretical insights concerning the role of personal integrity in fraudulent decision-making, presenting refined interventions that enhance comprehension of the underlying drivers of fraud occurrences and strategies for prevention. Furthermore, the study reveals a comprehensive three-part approach to improving organisational health through strengthening compliance mechanisms and cultivating an ethical-values-based culture.

The study introduces a novel conceptual framework, the personal ethic-based fraud motivation model, which offers a deeper understanding of the factors and conditions influencing individuals’ propensity to engage in fraudulent activities. Furthermore, this study presents a three Cs strategy that effectively delineates the influential forces that drive individuals to surmount fraud risks.

This study aims to assess the effectiveness of forensic accounting techniques to prevent and detect fraudulent activities in firms in Pakistan.

A descriptive research approach has been adopted in this study. Primary data has been collected through structured questionnaires distributed to professionals from investigating firms, professional bodies and field researchers. The independent variables that were analyzed included fraud investigation, litigation support and dispute resolution, whereas the dependent variables were fraud detection and prevention. The Statistical Package for Social Sciences has been used for data analysis to derive objective results.

This research reveals that forensic accounting techniques such as fraud investigation, litigation support and dispute resolution have a positive impact on fraud detection and prevention in Pakistani firms.

Firms should train staff on forensic accounting techniques, implement fraud risk management and anti-corruption policies, conduct regular financial statement audits and develop a whistleblower protection program to encourage employees to report fraudulent activities. The government should develop regulations and guidelines to promote the use of forensic accounting in firms.

This study is covering the gap in literature on financial fraud and forensic accounting practices concerning emerging economies such as Pakistan. This study can serve as a valuable resource for firms and policymakers to strengthen their fraud prevention efforts and build a more robust culture of financial integrity.