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Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

This study aims to examine the effect of professional commitment, commitment to ethics, internal locus of control and emotional intelligence on the ability to detect fraud through reduced audit quality behaviors.

The analysis unit is the internal auditor in internal control unit at state Islamic religious higher education in Indonesia. Data processing used covariance-based structural equation modeling using Lisrel Software and the Sobel test to verify the direct and indirect effects.

This study found empirical evidence that professional commitment and emotional intelligence positively impact the ability to detect fraud. Commitment to ethics and emotional intelligence has a negative effect on reduced audit quality behaviors. Furthermore, this study also provides that commitment to ethics and emotional intelligence indirectly impacts on the ability to detect fraud through reduced audit quality behaviors.

The organization periodically monitors auditors’ behaviors, especially reduced audit quality behaviors, during the audit process and encourages regulators to formulate policies related to increasing the ability to detect fraud.

This study provides knowledge regarding the driving force of internal auditors to mitigate reduced audit quality behaviors and increase the ability to detect fraud.

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.

In a virtual interconnected digital space, the metaverse encompasses various virtual environments where people can interact, including engaging in business activities. Machine learning (ML) is a strategic technology that enables digital transformation to the metaverse, and it is becoming a more prevalent driver of business performance and reporting on performance. However, ML has limitations, and using the technology in business processes, such as accounting, poses a technology governance failure risk. To address this risk, decision makers and those tasked to govern these technologies must understand where the technology fits into the business process and consider its limitations to enable a governed transition to the metaverse. Using selected accounting processes, this study aims to describe the limitations that ML techniques pose to ensure the quality of financial information.

A grounded theory literature review method, consisting of five iterative stages, was used to identify the accounting tasks that ML could perform in the respective accounting processes, describe the ML techniques that could be applied to each accounting task and identify the limitations associated with the individual techniques.

This study finds that limitations such as data availability and training time may impact the quality of the financial information and that ML techniques and their limitations must be clearly understood when developing and implementing technology governance measures.

The study contributes to the growing literature on enterprise information and technology management and governance. In this study, the authors integrated current ML knowledge into an accounting context. As accounting is a pervasive aspect of business, the insights from this study will benefit decision makers and those tasked to govern these technologies to understand how some processes are more likely to be affected by certain limitations and how this may impact the accounting objectives. It will also benefit those users hoping to exploit the advantages of ML in their accounting processes while understanding the specific technology limitations on an accounting task level.

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.

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

This study aims to investigate Bangladesh’s e-commerce regulations in light of the growing criticism that they are insufficient to curb predicate crimes like fraud and money laundering in the online marketplace.

This study used the exploratory design to examine the latest ministerial directives and laws governing e-commerce in Bangladesh to determine why they cannot prevent fraudulent activities in this promising sector and identify potential solutions.

Bangladesh’s regulatory responses to e-commerce fraud prevention and detection are reactive and inadequate. Regulators are unwilling and unable to enforce available legal provisions for various reasons, including a lack of knowledge and coordination among the agencies.

This paper focuses solely on the legal and regulatory framework in place to combat e-commerce fraud. Other critical issues, such as consumer rights, privacy and data protection in e-commerce, are not addressed.

The findings of this study will assist policymakers in revising current regulatory approaches to e-commerce to protect this sector from criminal abuse.

This study looked into the possibility of using a proactive risk-based approach in the e-commerce sector, similar to what the Bangladesh Financial Intelligence Unit does in the financial sector.

The 2008 Chinese melamine milk scandal resulted in six reported fatalities and affected around 300,000 children, of whom 54,000 were hospitalised. Previous studies have used linear approaches to examine the root causes of the melamine milk scandal.

In the present study, we applied a systems approach to the melamine milk scandal to identify the complex systems-level failures across the supply chain leading to the incident and why food fraud incidents such as this occurred in the dairy sector. Additionally, systemic failures associated with food fraud vulnerability factors were considered (i.e. opportunities, motivation and control measures).

48 contributory factors of influence were identified and grouped across six sociotechnical levels across the Chinese dairy system, from government to equipment and surroundings. Lack of vertical integration (processes and communication) contributed to the failure. When viewed from a broader perspective, the melamine milk scandal can be linked to a series of human errors and organisational issues associated with government bodies, the dairy supply chain, individual organisations and management decisions and individual actions of staff or processes.

This approach is of value to policymakers and the industry as it supports public health investigations of food fraud incidents and proactive food safety management.

To the best of our knowledge, this is the first study to analyse a food safety or fraud incident using the AcciMap approach and the food fraud vulnerability assessment (FFVA) technique. AcciMap analysis is applied to both unintentional and intentional aspects of the incident.

This study aims to examine the effect of audit client’s use of blockchain (BC) on auditing accounting estimates (AEs), especially the inherent risk (IR), control risk (CR) and collection of audit evidence.

The study used a questionnaire to collect data for a sample of 249 auditors. A partial least squares method is used to test the hypotheses.

The results showed positive relationship between audit client’s use of BC and both IR and CR when auditing AEs. The results also showed the BC improves the collection of sufficient and appropriate audit evidence when auditing AEs.

This study did not address all the risks associated with auditing AEs, including fraud, detection, sampling and nonsampling risks, and the procedures and tests for auditing AEs.

There are several implications of this research, including that it informs the revision of auditing standards and guidelines to correspond with successive technological changes, which subsequently clarify the roles and responsibilities of auditors, and the study findings will also cause changes to the design and form of audit procedures so as to obtain sufficient and appropriate audit evidence.

To the best of the authors’ knowledge, this study is considered the first of its kind that deals with the effects of audit client’s use of BC on audit AEs in the Middle East and North Africa region. This study also presented different sets of measures as proxies for measuring IR, CR and AE.

This study aims to investigate the factors that influence asset misappropriation fraud among police officers in the Internal Security and Public Order (Malay: Keselamatan Dalam Negeri dan Ketenteraman Awam [KDNKA]) Department, Royal Malaysian Police (RMP).

Primary data were collected from a questionnaire-based survey and the data were analysed using descriptive analysis, correlation analysis and multiregression.

The element of pressure was found to have a negative relationship with asset misappropriation fraud. This result shows the uniqueness of the KDNKA Department in the RMP. The other three elements are found to have a significant positive relationship with asset misappropriation fraud.

This study’s findings can aid academicians in gaining a better understanding of the literature surrounding fraud diamond theory and aid the policymakers in recognizing the signs and possibilities of asset misappropriation fraud in public agencies.

To the best of the authors’ knowledge, this is the first study that uses the KDNKA Department which is the second largest department in RMP as a population in fraud study.