Search results

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.

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.

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 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.

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 study is to examine whether executive compensation structure is a predictor of a value judgment shift facilitating fraud. The Raval (2018) disposition-based fraud model theorizes that in a fraud, a judgment shift occurs that results in an intentional action. Judgment shifts are influenced by intertemporal rewards, an executive compensation structure comprising salary (immediate reward) and delayed compensation in performance-based incentives.

Using an archival data set consisting of frauds identified through Securities and Exchange Commission Accounting and Auditing Enforcement Releases, the compensation structure of executives involved in frauds is compared against the compensation structure of executives in a peer control group.

There was a significant difference in the intertemporal rewards of the compensation structures between the two groups, indicating that compensation structure presents intertemporal choices leading to a judgment shift that influences the deliberate action of fraud.

This study represents the first empirical test of the disposition-based fraud model using intertemporal rewards leading to judgment shift.

Executive compensation structure should reduce intertemporal rewards for executives reducing judgment shifts that can result in risk of fraud.

This study addresses how executive compensation structure can result in fraud.

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 propose a law enforcement strategy for investment fraud through comparative studies in the United States of America (USA), Canada and Indonesia, and to identify the factors that cause weak law enforcement on investment fraud with the object of a binary options case study in Indonesia.

This research is a type of legal research, namely, research based on legal materials (library-based). The legal materials used include primary legal materials and secondary legal materials. The approaches used are the statute approach, the case approach and the comparative approach. The data collection technique used in this research is a literature study. The analysis was carried out qualitatively by using an interactive model.

In 2022, the Indonesian Financial Services Authority (OJK) recorded that the total value of public losses because of investment fraud in Indonesia reached 117.4tn IDR. Weak law enforcement is the reason investment fraud thrives in society. Strategies that can be implemented to prevent investment fraud include early detection of new investment fraud modes through the whistleblower program, mutual legal assistance in criminal matters, criminal restitution and improvement of public financial literacy.

This study examines the problems of law enforcement against investment fraud with a case study of binary options in Indonesia. A law enforcement strategy is built on identifying issues and adopting law enforcement policies against investment fraud in Canada and the USA.

For individuals, the results of this research can be used as reading material to increase their understanding of investment fraud. For the government, the results of this study can be a reference in an effort to eradicate the rise of investment fraud cases more effectively and create a safe digital economic space for investors.

The results of this study are expected to be useful in providing recommendations for strategies to strengthen law enforcement against the problems of investment fraud cases so as to form a conducive investment climate in the sense of being safe, comfortable and profitable.

Legal frameworks to prevent investment fraud are rarely discussed. The rise in binary options cases that occur is an indication of weak law enforcement in the investment sector. Therefore, an in-depth study of law enforcement strategies to prevent investment fraud is needed, with comparative studies in the USA, Canada and Indonesia.

This study aims to examine the most influential drivers, both product-specific and consumer-specific, affecting Asian consumers' willingness to pay (WTP) for organic olive oil.

To individuate the most influential drivers of WTP for organic products and to assess their effect, in terms of configurational paths and consumer profiles, this study sequentially employs explorative factor analysis approach and a fuzzy-set qualitative comparative analysis method. The survey is carried out in different areas of Asia (e.g. Pakistan, Vietnam and China).

The results suggest that Asian consumers' WTP for organic products is described by consumer-specific drivers (gender, occupation and household size) as well as product-specific drivers (product authenticity and sustainability, consumer ethnocentrism and food fraud risk perception).

The findings of the study permit the identification of different drivers that move consumers' WTP for organic olive oil. The study contributes to setting the ground for companies to propose and implement efficacious marketing strategies for organic olive oil in importing countries, such as Asia.