Search results

The purpose of this paper is to analyze FTX cryptocurrency frauds. FTX is a former cryptocurrency exchange platform that went bankrupt because of fraud in 2022.

Using a qualitative method and a case study of FTX, the authors document the multiple fraud schemes perpetrated. The authors collected media and research articles that discussed the FTX case. The authors analyzed 18 articles.

Based on this case, the authors highlight the governance and ethics weaknesses in the FTX environment. The authors also discuss cryptocurrency risks and regulation of cryptocurrencies. The FTX affair has shaken up the international regulatory world, which has been seeking solutions to protect customers and investors and helping banks take positions since 2022.

This study contributes to the fraud literature by deeply examining cryptocurrency fraud risks. In addition, the findings could help financial institutions and guide them in the cryptocurrency world.

The aim of this study is to examine the patterns of fraud present in Valeant’s 2014 and 2015 financial statements and determine through a risk management analysis whether these frauds could have been prevented. This analysis provides the opportunity to more effectively prevent financial statement fraud.

Data were collected from Valeant pharmaceuticals annual reports, financial statements reports and financial authority documentation. Based on these documents, this paper analyzes the different fraud schemes and investigate whether fraud could have been detected earlier by governance actors. In particular, this paper examines the firm’s financial statements three years before the fraud was detected by the Securities and Exchange Commission.

The analysis of financial statements reveals few clues and no alarming red flags three years before detection of the fraud. However, financial statement analyses were complex because of the many acquisitions the firm made in the years before.

This paper aims to contribute to the literature on fraud by investigating a case of financial statement fraud.

This study aims to investigate the role of freight forwarding companies in detecting and reporting trade-based money laundering. The proximity of freight forwarding companies to shipping-related trade-based money laundering red flags places them in an ideal position to detect suspicious transactions.

The study used semi-structured interviews with expert participants in freight forwarding shipping and compliance aspects around freight forwarding. This study focuses on the South African context.

Freight forwarding companies are well-positioned to detect, investigate and report on trade-based money laundering schemes. However, the companies are not always aware of the guidelines designed to assist in identifying trade-based money laundering schemes. Thus, freight forwarding companies develop internal processes to identify trade anomalies but are often unable to link trade anomalies to illegal financial flows and trade-based money laundering schemes.

The current regulations on money laundering can be extended to freight forwarding companies by the respective regulators for enhanced anti-money laundering protection. This study is limited to freight forwarding companies in a South African context.

Increased awareness among staff in freight forwarding companies can assist them in identifying trade-based money laundering red flags to detect and prevent trade-based money laundering schemes.

This paper assists other role players and policymakers in the trade process to create a better awareness of trade-based money laundering. The limited obligations on freight forwarding companies to comply with anti-money laundering regulations lead to a more volunteer-like compliance practice.

To the best of the authors’ knowledge, this is the first paper that offers insight into the role of freight forwarding companies in detecting trade-based money laundering in South Africa.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

This study examines how corporate litigation, both securities-related and not, is affected by hedge fund (HF) activism.

We use a difference-in-differences (DiD) method, along with propensity score matching and firm fixed effects and a comparison of HF and non-HF activists for identification.

We find that companies that are targeted by HFs face operation-related lawsuits, mainly from stakeholders or competitors. This effect does not seem to be caused by targets' higher tendency to settle the cases. Our evidence shows that HF activists increase firm value for the target firms that are prone to litigation.

Therefore, our evidence supports the idea that the higher operation litigation risks are unintended consequences of improving firm efficiency through cost savings or restructuring of target firms by the activists.

The purpose of this paper is to examine whether mandatory disclosure of information accompanying the sale of real estate achieves its aim of informed purchasers.

Using a case study approach focused on mandatory disclosure in South Australia data was collected from interviews and focus groups with key personnel in the property industry involved in the production of information required to fulfil vendors’ disclosure obligations.

The authors found that purchasers are ill-served by a long and complex form of mandatory disclosure with a short time frame that prevents the use of the information provided. Without good form design and increased digital affordances provided by the cadastral and conveyancing systems, mandatory disclosure is insufficient to ensure minimisation of information asymmetry between vendor and purchaser.

To the best of the authors’ knowledge, this is the first Australian qualitative study that examines the utility of mandatory vendor disclosure in real estate sales and the first to consider the impact of the digitalisation of cadastral and conveyancing systems upon the efficacy of mandatory disclosure regimes.

This study aims to understand the perceived emotions of human–artificial intelligence (AI) interactions in the private sector. Moreover, this research discusses the transferability of these lessons to the public sector.

This research analysed the comments posted between June 2022 and June 2023 in the global open Reddit online community. A data mining approach was conducted, including a sentiment analysis technique and a qualitative approach.

The results show a prevalence of positive emotions. In addition, a pertinent percentage of negative emotions were found, such as hate, anger and frustration, due to human–AI interactions.

The insights from human–AI interactions in the private sector can be transferred to the governmental sector to leverage organisational performance, governmental decision-making, public service delivery and the creation of economic and social value.

Beyond the positive impacts of AI in government strategies, implementing AI can elicit negative emotions in users and potentially negatively impact the brand of private and government organisations. To the best of the authors’ knowledge, this is the first research bridging the gap by identifying the predominant negative emotions after a human–AI interaction.

The effectiveness of independent directors in making autonomous decisions for better corporate governance in organizations has often been questioned. This paper aims to investigate their role in company’s decision making in India and the reasons behind their ineffectiveness.

This paper examines the regulatory environment and ongoing reforms in which independent directors operate. It identifies crucial factors such as ownership patterns, the appointment and selection process that affect their autonomy. The analysis draws from newspaper articles, blogs, India’s regulatory requirements, The Companies Act and relevant related literature.

The findings reveal that the independence of directors remains largely in form but not in function. This paper recommends a fair and more robust selection through an independent authority, and disclosure of the resignations of independent directors. Independent directors should be given more powers and their risk-reward scheme should be analyzed.

The paper emphasizes the need for independent directors to be truly independent from the senior management, promoters, and other existing directors. It calls for tighter and more transparent appointment procedures to ensure that independent directors are not influenced by senior management and can bring objectivity to the company board.

Between 2006 and 2012, Mexico implemented a “frontal war against organized crime”. This strategy increased criminal violence and triggered negative consequences across the country’s economic, political and social spheres. This study aims to analyse how the magnitude and visibility of criminal violence impact the housing market of Mexico City.

The authors used different violent proxies to measure the effect of the magnitude and visibility of violence in housing prices. The structure of the data set is an unbalanced panel with no conditions of strict exogeneity. To address endogeneity, the authors calculate the first differences to estimate an Arellano–Bond estimator and use the lags of the dependent variable to instrumentalise the endogenous variable.

Results suggest that the magnitude of violence negatively impacts housing prices. Similarly, housing prices are negatively affected the closer the property is to visible violence, measured through narcomessages placed next to the bodies of executed victims. Lastly, housing prices are not always affected when a violent event occurs nearby, specifically, when neighbours or potential buyers consider this event as sporadic violence.

There are only a few studies of violence in housing prices using data from developing countries, and most of these studies are conducted with aggregated data at the municipality or state level. The authors are using geocoded information, both violence events and housing prices, to estimate more disaggregated effects. Moreover, the authors used different proxies to measure different characteristics of violence (magnitude and visibility) to estimate the heterogeneous effects of violence on housing prices.

This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.