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This study aims to, firstly, develop a red flag checklist for cryptocurrency Ponzi schemes and, secondly, to test this red flag checklist against publicly available marketing material for Mirror Trading International (MTI). The red flag checklist test seeks to establish if MTI’s marketing material posted on YouTube^® (in the form of a live video presentation) exhibits any of the red flags from the checklist.

The study uses a structured literature review and qualitative analysis of red flags for Ponzi and cryptocurrency Ponzi schemes.

A research lacuna was discovered with regard to cryptocurrency Ponzi scheme red flags. By means of a structured literature review, journal papers were identified that listed and discussed Ponzi scheme red flags. The red flags from the identified journal papers were subsequently used in a qualitative analysis. The analyses and syntheses resulted in the development of a red flag checklist for cryptocurrency Ponzi schemes, with five red flag categories, containing 18 associated red flags. The red flag checklist was then tested against MTI’s marketing material (a transcription of a live YouTube presentation). The test resulted in MTI’s marketing material exhibiting 88% of the red flags contained within the checklist.

The inherent limitations in the design of using a structured literature review and the lack of research regarding the cryptocurrency Ponzi scheme red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes. The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes.

According to the Association of Certified Fraud Examiners, financial statement fraud represents the smallest amount of fraud cases but results in the greatest monetary loss. The researcher previously investigated the characteristics of financial statement fraud and determined the presence of 16 fraud indicators. The purpose of this study is to establish whether investors and other stakeholders can detect and identify financial statement fraud using these characteristics in an analysis of a company’s annual report.

This study analyses a financial statement fraud case, using the same techniques that were previously applied, including horizontal, vertical and ratio analysis. These are preferred because stakeholders have relatively easy access to them.

The findings show several fraud characteristics, with a few additional ones not previously found prevalent. Financial statement fraud thus tends to differ between cases. It is also easier to detect and identify fraud indicators ex post facto.

This study is a practical case showing that financial statement fraud can be detected and identified in the financial statements of companies that commit fraud.

Illegal wildlife trade (IWT) is a transnational organized crime that generates billions in criminal proceeds each year. Yet, it is not regarded by many countries as a serious crime. There is also no general consensus on its recognition as a predicate offence for money laundering. In this regard, banks are misused in different ways to facilitate financial flows linked to IWT. This paper aims to illustrate the importance of the banking sector in combating money laundering relating to IWT. It also aims to demonstrate the need for a general recognition of IWT as a predicate offence for money laundering.

This study investigates the implementation of money laundering controls by banks in the illegal-wildlife-trade context. As background to this investigation, it provides an overview of IWT, which is followed by an exploration of some of the general characteristics of the banking sector, before discussing the relevant Financial Action Task Force (FATF) recommendations.

This study finds that the banking sector is well-placed to combat money laundering relating to the IWT and is, by virtue of its international nature and strong focus on compliance, able to be effective in preventing the use of the proceeds of IWT as well as in identifying broader trafficking networks. Moreover, the banking sector is well-equipped to develop appropriate platforms to facilitate the swift, easy and effective sharing of financial intelligence between banks at the local, regional and especially international level.

This study draws on publicly available information on financial flows relating to IWT. Little data and research are available on the financial flows and consequently the money laundering techniques used in cases suspected of IWT.

There has been little scholarly research on the relationship between money laundering and the IWT as well as the financial flows of IWT in general. This study highlights some of the money laundering techniques used in relation to IWT by drawing on the works of various international organizations, including the FATF.

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.

Using an ecological model of child participation and drawing on newsletter data from schools across the United States of America (USA), this chapter statistically explores five state factors linked with school protests against gun violence: (1) children’s neighbourhood opportunity; (2) race/ethnicity; (3) voter preference for either a Republican or a Democratic president; (4) child participation policies; and (5) gun laws/violence/ownership. The chapter explores factors linked to both student participation in protests and student nonparticipation in protests that take place at their schools. Three factors were found to be associated with participation and nonparticipation: children’s neighbourhood opportunity, voters’ preference, and participation policies. Findings suggest that Democratic-voting states, mediated by education opportunity, predict the frequency of student protests against gun violence. In Republican-voting states, where education opportunity does not mediate the frequency of school protests, students still organised and participated in protests but to a lesser extent. In addition, states with high overall children’s neighbourhood opportunity and voting student education board members are highly likely to have non-protesting students in schools with protests. The chapter presents five conclusions from these results for the positive and negative exercise of child participation rights and considers what further multilevel explorations can be done to further test the framework employed for this analysis.

The primary purpose of this exploratory paper is to propose a novel analytical framework for examining corruption from a behavioral perspective by highlighting multiple issues associated with consumerism.

This paper examines the relationship between excessive consumption activities and corrupt acts, drawing upon existing literature on corruption, consumerism and consumption, as well as multiple reports and cases of corruption and money laundering in Indonesia. With regard to corruption networks, this paper analyses the associated behavioral patterns and social dynamics by using the Fraud Triangle and the Fraud Elements Triangle frameworks to examine the phenomenon of living beyond one’s means. This paper also addresses the notion of sacredness in the context of consumer activities and how such sacredness plays a role in causing otherwise honest individuals to engage in corrupt acts.

The author established that corruption represents a complex societal issue that extends across several dimensions of society, encompassing both horizontal and vertical aspects. Consequently, addressing this problem poses significant challenges. Excessive consumption has been identified as one of the various behavioral concerns that are implicated in the widespread occurrence of corruption in many nations. Individuals who partake in excessive consumption play a role in shaping ethical norms that serve to legitimize and rationalize immoral behavior, therefore fostering a society marked by corruption. The act of engaging in excessive consumption is also associated with cases of money laundering offenses that are connected to corruption and several other illicit activities. The lifestyle of corrupt individuals is one of the primary behavioral concerns associated with corruption, as “living beyond means” is the most common behavioral red flag among occupational fraud offenders worldwide. The phenomenon of consumerism may also shape the minds of individuals as if it were an “implicit religion” due to the fact that it may generate human experiences that elicit highly positive emotions and satisfy certain sacredness-associated characteristics. The pursuit of transcendental experiences through the acquisition and consumption of sacred consumption objects may heighten the incentive to commit fraudulent acts such as corruption.

This self-funded exploratory study uses document analysis to examine the corruption phenomenon in Indonesia. Future studies will benefit from in-depth interviews with former offenders and investigators of corruption.

This exploratory study contributes to advancing corruption prevention strategies. It does this by introducing a novel analytical framework that allows for the examination of several behavioral issues associated with consumerism, which have the potential to foster the proliferation of corruption.

This exploratory study highlights the importance of comprehending the intricacies of consumerism, namely, its adverse effects on the proliferation of corruption.

The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

The purpose of this study is to provide a firsthand perspective on the challenges and risks that can arise in arbitration proceedings.

To investigate the concrete methods money launderers use, a qualitative study was conducted with 10 alleged money launderers and 18 prevention experts. The results were then tested quantitatively, and it was concluded that among money launderers, the highly regulated financial sector is less popular than other sectors.

Money launderers relocate to unregulated sectors or offshore banks to avoid being questioned by compliance officers. Therefore, it is necessary for arbitrators involved in commercial or investor–state arbitration to have the expertise to readily identify the issues raised by these criminal law concepts and provide answers.

This paper examines the intersection between the areas of international commercial arbitration and money laundering, bribery, as well as embezzlement. At the same time, it draws attention to the need to analyze compliance issues in arbitration proceedings.

This study aims to synthesize the existing literature with insights gained from interviews conducted with regulatory experts. The objective is to analyse the challenges associated with incorporating cryptocurrencies into regulatory frameworks and to explore constraints in the regulatory institutionalization of cryptocurrencies.

The study methodology consists of two steps. The first step is to identify regulatory constraints in the literature review and in the next step, interviews are conducted with officials of the State Bank of Pakistan (SBP). The study used a qualitative case study methodology, in which a single case (regulatory constraint) was selected as a unit of analysis.

The findings show that lack of traceability, legal status, lack of governmental control due to decentralization, difficulty enforcing laws, volatility, lack of skills with regulators and difficulty integrating cryptocurrencies into the current financial system are the main obstacles to the introduction of a regulatory framework. Thus, on a broader conceptual level, the findings can be grouped into opportunism, lack of strategic capability and fragmented global laws.

This study could inform global cryptocurrency regulation discussions, sharing a developing country’s views on balancing the government, central banks, the financial sector and public interests. This could guide countries to consider cryptocurrency adoption in similar situations. This could affect the cryptocurrency market, impacting demand, supply and investor trust in Pakistan.

The study has implications for policy making officials. The research aims to offer valuable insights to the SBP and other regulatory authorities, helping them identify potential risks and create an effective regulatory framework for cryptocurrencies.

The study has implications for society in knowing about the volatile nature of cryptos and anonymity of their issuers, which poses regulatory constraints. This then implies its harmfullness to its traders and the huge losses that may arise from their trading due to its volatile nature.

This study contributes to the literature on the constraints, responsibilities and consultation framework of cryptocurrency regulations.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.