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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 study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.

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.

The fraud landscape for FinTech industry has increased over the past few years, certainly during the time of COVID-19, FinTech market reported rapid growth in the fraud cases (World Bank, 2020). Taking the consideration, the paper has qualitatively understood the loopholes of the FinTech industry and designed a conceptual model declaring “Identity Theft” as the major and the common fraud type in this industry. The paper is divided in two phases. The first phase discusses about the evolution of FinTech industry, the second phase discusses “Identity Theft” as the common fraud type in FinTech Industry and suggests solutions to prevent “Identity Theft” frauds. This study aims to serve as a guide for subsequent investigations into the FinTech sector and add to the body of knowledge regarding fraud detection and prevention. This study would also help organisations and regulators raise their professional standards in relation to the global fraud scene.

This paper revisits the literature to understand the evolution of FinTech Industry and the types of FinTech solutions. The authors argue that traditional models must be modernised to keep up with the current trends in the rapidly increasing number and severity of fraud incidents and however introduces the conceptual model of the common fraud type in FinTech Industry. The research also develops evidences based on theoretical underpinnings to enhance the comprehension of the key fraud-causing elements.

The authors have identified the most common fraud type in the FinTech Industry which is “Identity Theft” and supports the study with profusion of literature. “Identity theft” and various types of fraud continue to outbreak customers and industries similar in 2021, leaving several to wonder what could be the scenario in 2022 and coming years ahead (IBS Inteligence, 2022). “Identify theft” has been identified as one the common fraud schemes to defraud individuals as per the Association of Certified Fraud Examiners. There is a need for many of the FinTech organisations to create preventive measures to combat such fraud scheme. The authors suggest some preventive techniques to prevent corporate frauds in the FinTech industry.

This study identifies the evolution of FinTech industry, major evidences of Identity Thefts and some preventive suggestions to combat identity theft frauds which requires practical approach in FinTech Industry. Further, this study is based out of qualitative data, the study can be modified with statistical data and can be measured with the quantitative results.

This study would also help organisations and regulators raise their professional standards in relation to the global fraud scene.

This study will serve as a guide for subsequent investigations into the FinTech sector and add to the body of knowledge regarding fraud detection and prevention.

This study presents evidence for the most prevalent fraud scheme in the FinTech sector and proposes that it serve as a theoretical standard for all ensuing comparison.

The main purpose of this study is to test for the interaction effect of digital literacy in the relationship between financial technologies (FinTechs) of biometrics and mobile money and digital financial inclusion among the unbanked poor women, youth and persons with disabilities (PWDs) in rural Uganda.

Covariance-based structural equation modeling was used to construct the interaction effect using data collected from the unbanked poor women, youth and PWDs located in the four regions in Uganda as prescribed by Hair et al. (2022).

The findings from this study are threefold: first; the results revealed a positive interaction effect of digital literacy between FinTechs of biometrics and mobile money and digital financial inclusion. Second; the results also confirmed that biometrics identification positively promotes digital financial inclusion. Lastly; the results showed that mobile money positively promotes digital financial inclusion. A combination of FinTechs of biometrics and mobile money together with digital literacy explain 29% variation in digital financial inclusion among the unbanked poor women, youth and PWDs in rural Uganda.

The data for this study were collected mainly from the unbanked poor women, youth and PWDs. Further studies may look at data from other sections of the vulnerable population in under developed financial markets. Additionally, the data for this study were collected only from Uganda as a developing country. Thus, more data may be obtained from other developing countries to draw conclusive and generalized empirical evidence. Besides, the current study used cross sectional design to collect the data. Therefore, future studies may adopt longitudinal research design to investigate the impact of FinTechs on digital financial inclusion in the presence of digital literacy across different time range.

The governments in developing countries like Uganda should support women, youth, PWDs and other equally vulnerable groups, especially in the rural communities to understand and use FinTechs. This can be achieved through digital literacy that can help them to embrace digital financial services and competently navigate and perform digital transactions over digital platforms like mobile money without making errors. Besides, governments in developing countries like Uganda can use this finding to advocate for the design of appropriate digital infrastructures to reach remote areas and ensure “last mile connectivity for digital financial services' users.” The use of off-line solutions can complement the absence or loss of on-line network connectivity for biometrics and mobile money to close the huge digital divide gap in rural areas. This can scale-up access to and use of financial services by the unbanked rural population.

This paper sheds more light on the importance of digital literacy in the ever complex and dynamic global FinTech ecosystem in the presence of rampant cyber risks. To the best of the authors' knowledge, limited studies currently exist that integrate digital literacy as a moderator in the relationship between FinTechs and digital financial inclusion, especially among vulnerable groups in under-developed digital financial markets in developing countries. This is the novelty of the paper with data obtained from the unbanked poor women, youth and PWDs in rural Uganda.

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.

Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.

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 prevention of fraudulent activities, particularly within a financial context, is of paramount significance in all spheres, as it not only impacts the sustainability of corporate entities but also has the potential to have a broader economy-wide impact. This paper aims to focus on dual implications associated with financial distress, the first being associated with the temptation to launder funds due to financial distress, and the second being the potential for illicit activities, such as fraud, money laundering or terror financing, to give rise to financial distress.

The paper examines the literature on financial distress and uses theories of financial crime to establish a link between financial distress and financial crime.

In recent years, there has been a surge in corporate financial distress, particularly in the aftermath of concurrent crises such as the COVID-19 pandemic and the Russia–Ukraine war. Through a comprehensive examination of literature pertaining to financial distress and financial crime, this study identifies a proclivity towards fraudulent conduct arising from instances of financial distress. Moreover, the engagement in such illicit activities subsequently exacerbates the financial distress. An analysis of the relationship between financial crime and financial distress reveals the existence of a vicious cycle between the two.

The results of this study have the potential to advance understanding of the relationship between financial distress and financial crime, which has been previously underexplored.

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.