Judges reject claims that defendants defrauded agencies by not disclosing China ties
Judges reject claims that defendants defrauded agencies by not disclosing China ties
Introduction: Predatory journals, with low standards of publication, mean flawed or fraudulent research can compromise future research. Often called predatory or deceptive publishers, both of these ...terms have an implication that the editors and publishers behind them have a motivation to deceive or con authors. However, the motivations remain an assumption because little is known about the individuals behind these journals. This research intended to use qualitative, in-depth interviews to find out more about the individuals behind predatory journals. By engaging with them directly, we hoped to gain an understanding of how they see themselves in the publishing landscape, what value they add and how they achieve these aims. Methods: E-mails received by the authors were mined for contact information of suspected predatory journals. Over the course of a year, 2804 e-mails were sent inviting respondents to an interview, for which there would be a small monetary compensation. Results: Despite sending 2804 emails, only three responses were received, and all three did not schedule an interview when prompted. Two of the three requested that a translator be present. A significant percentage of the e-mails (40.8%) bounced back, indicating the contact information was not valid. Discussion: While the information gained was limited, it would appear many are aware of the dubious nature of their journal and do not wish further scrutiny by being contacted. Others may lack the English language skills necessary to be engaged in basic written communication, let alone the more complex language and grammar of scientific publishing.
Financial fraud can have serious ramifications for the long-term sustainability of an organization, as well as adverse effects on its employees and investors, and on the economy as a whole. Several ...of the largest bankruptcies in U.S. history involved firms that engaged in major fraud. Accordingly, there has been considerable emphasis on the development of automated approaches for detecting financial fraud. However, most methods have yielded performance results that are less than ideal. In consequence, financial fraud detection continues as an important challenge for business intelligence technologies. In light of the need for more robust identification methods, we use a design science approach to develop MetaFraud, a novel meta-learning framework for enhanced financial fraud detection. To evaluate the proposed framework, a series of experiments are conducted on a test bed encompassing thousands of legitimate and fraudulent firms. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Additional experiments demonstrate the effectiveness of the meta-learning framework over state-of-the-art financial fraud detection methods. Moreover, the MetaFraud framework generates confidence scores associated with each prediction that can facilitate unprecedented financial fraud detection performance and serve as a useful decision-making aid. The results have important implications for several stakeholder groups, including compliance officers, investors, audit firms, and regulators.
This work is devoted to the development of highly efficient tools for making decisions by banking structures to issue and maintain a loan. The developed methodology is intended to support banking ...services for lending to legal entities. The paper presents the results of a comprehensive analysis of existing methods that can be used to identify scammers, the results of the analysis of data types for solving this problem and ranking them in terms of efficiency. A methodology for building algorithms for searching for scammers is proposed and the application of an algorithm for graph analysis of legal entity relationships for detecting fraud is demonstrated.
The article is devoted to the study of the organizational and methodological foundations of ensuring economic security through the mechanism of preventing corporate fraud. The influence of ...“white-collar” crime at the micro level on the overall economic state of the state is considered. The theoretical aspects of corporate fraud are investigated and generalized. The analysis of existing methods and techniques to assess the risk of their occurrence in a particular enterprise is carried out. The emphasis is placed on the assessment of the external operating environment, considered as a source of potential threats to the company’s security, as well as on the study of the risk of corporate fraud in relation to a particular business entity. The authors propose mechanisms to protect the economic interests of the organization from various threats from the staff, which are essential for maintaining the level of its economic security. The findings on the relationship between corporate fraud and micro-level economic security are not conclusive. This indicates the need for further scientific work on the problem, which will logically continue the problems raised in the article.
•This survey includes the most popular and effective anomaly detection techniques.•Highlights recent advancements in semi-supervised and unsupervised learning.•Comprehensive discussion in financial ...fraud applications.•This survey will form a foundation for future research in the area.
With the rise of technology and the continued economic growth evident in modern society, acts of fraud have become much more prevalent in the financial industry, costing institutions and consumers hundreds of billions of dollars annually. Fraudsters are continuously evolving their approaches to exploit the vulnerabilities of the current prevention measures in place, many of whom are targeting the financial sector. These crimes include credit card fraud, healthcare and automobile insurance fraud, money laundering, securities and commodities fraud and insider trading. On their own, fraud prevention systems do not provide adequate security against these criminal acts. As such, the need for fraud detection systems to detect fraudulent acts after they have already been committed and the potential cost savings of doing so is more evident than ever. Anomaly detection techniques have been intensively studied for this purpose by researchers over the last couple of decades, many of which employed statistical, artificial intelligence and machine learning models. Supervised learning algorithms have been the most popular types of models studied in research up until recently. However, supervised learning models are associated with many challenges that have been and can be addressed by semi-supervised and unsupervised learning models proposed in recently published literature. This survey aims to investigate and present a thorough review of the most popular and effective anomaly detection techniques applied to detect financial fraud, with a focus on highlighting the recent advancements in the areas of semi-supervised and unsupervised learning.
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