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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.

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 purpose of this paper is to add to the understanding of the human personality of fraudsters. This paper will explore their human personality by reviewing three characters from realist novels that have fraudsters as their leading characters. This pa[er will also contribute to literature that intersects between the humanities and criminology.

This paper reviews three fraudster characters from realist novels to explore their human personality, which includes qualitative phenomena resistant to positivist research.

Literature character review that adds to understanding of the qualitative nature of the personality of fraudsters. This qualitative nature of the human personality has been neglected in fraud research and the findings contribute to expanding understanding of the qualitative nature of fraud and fraudsters.

This paper is limited to a literature review from three characters from realist novels.

By expanding understanding of the human personality of fraudsters literary insights can contribute to fraud identification and prevention.

This paper reviews the human personality of three characters from novels to expand understanding of fraudsters, and thus contributes to the intersection of research between the humanities and criminology and fraud research.

This study aims to investigate the perceived willingness to adopt and use Forensic Accounting and Investigation Standards (FAIS) in Forensic Accounting and Investigation (FAI) assignments. The study also analyses the usefulness of FAIS in achieving the principle of natural justice (PNJ) concerning fairness.

The respondents comprised 118 accounting professionals whose online survey responses were analyzed descriptively. This study also uses a 2 × 2 contingency analysis representing two levels of usefulness and fairness.

The results revealed that FAIS 410 received the highest mean rating while FAIS 240 received the lowest mean rating in willingness to adopt and use FAIS, and most of the standards were related to the PNJ concerning fairness. The study shows the accounting professionals’ readiness to adapt and flourish with the help of these Standards in FAI assignments.

The findings of this study will increase practitioners’ awareness of the usefulness and fairness of FAIS, which will enhance their understanding of the significance of implementing these newly developed standards to harmonize the investigative process in forensic audits. Additionally, the findings may encourage regulators, researchers, accounting bodies and their members to adopt and conduct further FAIS studies that can advance financial crime prevention, detection and investigation knowledge.

This paper substantially contributes to the literature as it is the first to examine the usefulness and fairness of “Forensic Accounting and Investigation Standards” in the context of forensic audits and investigations, which has not been previously explored.

Every year, millions of consumers around the world become victims of credit card fraud. These individuals have to appeal to their credit card companies to reverse unauthorized charges. This study aims to profile the American consumers’ experience when complaints to their credit card companies about unauthorized charges fail to produce a resolution. Using a large database of consumer complaint filings with the Consumer Financial Protection Bureau (CFPB), the characteristics of these consumer complaints are identified, and the drivers of consumer financial hardship resulting from credit card fraud are determined.

A random sample of consumer complaints about their credit card companies’ perceived mishandling of cases, filed with the CFPB, is used to conduct content analysis. The resulting content analysis categories are used in a predictive model to determine the drivers of consumer hardship.

In nearly one-quarter of all complaint filings, the credit card company had blamed the complainant as the party responsible for the fraudulent charges or refused to open a fraud investigation altogether. Nearly 60% of complaint reports contain expressions of emotional distress and many mention financial hardship. Nearly half of all complainants consider the fraud department operations of their credit card company as lacking in service quality, many reporting inability to reach the department or to receive a returned call. Even after CFPB intermediation, only 15% of complainants receive some form of financial relief from their credit card company. The majority of the complainants report a lack of willingness by the credit card company to reverse unauathorized charges, leaving the complainant financially responsible for them.

This study focused on data collected from consumers. Future research can expand the scope of inquiry by surveying the staff and executives in the fraud investigation departments of credit card companies to determine the norms of fraud investigation used within the industry.

This study sheds light on the financial hardship and emotional pains that consumers victimized by credit card fraud experience in dealing with their credit card companies.

To the best of the authors’ knowledge, this is the first study to empirically examine American consumers’ complaints about the fraud investigation operations of their credit card companies. Using data captured through the complaint filing system of a federal bureau (CFPB), the findings have implications for policymakers, regulators and credit card companies.

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.

This paper aims to review corporate fraud, as a concept, and the emerging research trends in corporate fraud research from 1957 to 2022 using bibliometric analysis techniques.

A total of 7,750 publications from the Scopus database were first assessed using performance analysis to explore the descriptive nature of the bibliographic data, and subsequently, citation, co-citation, co-occurrence and bibliographic coupling analyses were conducted using the VOSviewer software.

The results indicate there has been increasing growth in fraud research over the years, especially since the global corporate scandals of 2008. Although fraud is a global issue, the results suggest that most extant studies originate from developed economies, with a high level of collaboration amongst scholars in these countries. In addition, the co-occurrence analysis indicates that research into corporate fraud has largely focused on its determinants and corruption. The determinants identified are further clustered in the paper as individual, organizational and national-level factors.

The findings should inform practitioners and policymakers of the state of knowledge on corporate fraud which could be useful in developing strategies and policies to mitigate its occurrence.

The study points to the need for research collaborations among scholars in developing economies to increase investigations into the occurrences of fraud.

To the best of the authors’ knowledge, this is the first study to holistically assess the intellectual structure of corporate fraud studies from its inception and the trends over time.

The study aims to explore the reasons behind external auditors' failure to detect and report fraud.

Semi-structured interviews were conducted with twenty-four experienced Big 4 auditors.

The present study reveals power issues within audit firms and how some dishonest audit partners deal with auditors' concerns at the higher echelons. It also shows how auditors are pressured and intimidated by audit clients when fraud-related issues are raised. Further, it sheds light on ethical, governance and regulatory issues inhibiting auditors’ ability to detect or report fraud.

This study advances the audit literature by adding practice-based evidence on why external auditors fail to discover fraud.

The results draw policymakers' attention to the issues that inhibit external auditors' ability to discover fraud in practice which could help policymakers develop effective interventions. Additionally, it provides several recommendations which could aid policymakers and audit firms in designing effective audit reforms to resolve the fraud detection deficit.

This is the first study exploring external auditors' views on their failure to detect and report fraud and how the conflict of interests operates in the audit practice.

A crucial contemporary policy question for financial service organizations of being resilient across the globe calls for rethinking and renovating by adopting and adapting to the technologies of artificial intelligence (AI). The purpose of this study is to propose a policy framework for adoption of AI in the finance sector by exploring the driving factors through systems approach.

Based on literature review and discussions with experts from both industry and academia, nine enablers were shortlisted, which were used in the questionnaire survey to determine ranks of enablers. Further, the study developed the interpretive structural model (ISM) with the help of experts.

The ISM digraph developed with the help of the experts, resulted in the enablers like anticipated profitability, contactless solutions, credit risk management and software vendor support as dependent factors and stood at the top of the ISM. On the other hand, factors like availability of the data, technical infrastructure and funds are the most driving factors, which lie on the bottom of the ISM.

The study provides implications and policy recommendations for the practicing managers and government agencies approaching the digital transformation towards the adoption of AI in the finance ecosystem.

The paper uses the systems approach for the development of the ISM of the enabling factors for the adoption of AI technology. On the basis of the results, the study proposes a policy framework to accelerate the functioning of the finance ecosystem with AI technology.

The banking sector requires a major comeback with the series of bank frauds that has shook the nation. The rising non-performing assets (NPAs) and corporate frauds find their roots in the top-level management or executive levels. The purpose of this study to analyse the behavioural component with corporate governance lapses for creating a trail and to what extent it can contribute to forensic analysis to help reduce and prevent fraud in the future.

This study is investigative in nature. This study uses case study approach by taking into account the major Advance–NPA–Fraud cases over period of 2010–2022. RBI data for bank advances, NPAs and advances-relate frauds from 2005 to 2019 were studies and interpreted for creating a trend and pattern for the reduction and prevention of frauds.

The authors found that behavioural factors and personalities affect the systems and culture of the company, thereby giving a jolt to the corporate governance mechanisms along with various entities like depositors, consumers and shareholders.

Assessing the behavioural aspects for risk mitigation remains unexplored in the banking sector. The personality dimension can help in contributing to comprehending the mental aspects and the reasons behind the combination of dark triads with economic offences.

This study is beneficial to all the beneficiaries of the banking sector and the economy at large in understanding the implications of risks because of patterns formed by emotions and vulnerability towards economic and fugitive economic crimes.