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Money men : a hot start-up, a billion-dollar fraud, a fight for the truth
'Money Men' is the astonishing inside story of Wirecard's multi-billion-dollar fraud, Europe's biggest new tech darling revealed as a house of cards. Uncovering fake bank accounts, fake offices and possibly even a fake death, McCrum offers a searing exposé that will finally lay bare the truth.
Book
Praxishandbuch Insolvenzstrafrecht
Das Handbuch erläutert das materielle Insolvenzstrafrecht unter Einbeziehung des Strafprozessrechts sowie der Grundzüge des Handels-, Gesellschafts- und Insolvenzrechts. Die 2. Auflage berücksichtigt insbesondere die neuen europäischen Gesellschaftsformen (SE, SUP, limited) und die Auswirkungen der Finanz- und Wirtschaftskrise.
eBook
Shaping Democratic Practice and the Causes of Electoral Fraud: The Case of Nineteenth-Century Germany
Why is there so much alleged electoral fraud in new democracies? Most scholarship focuses on the proximate cause of electoral competition. This article proposes a different answer by constructing and analyzing an original data set drawn from the German parliament's own voluminous record of election disputes for every parliamentary election in the life of Imperial Germany (1871–1912) after its adoption of universal male suffrage in 1871. The article analyzes the election of over 5,000 parliamentary seats to identify where and why elections were disputed as a result of “election misconduct.” The empirical analysis demonstrates that electoral fraud's incidence is significantly related to a society's level of inequality in landholding, a major source of wealth, power, and prestige in this period. After weighing the importance of two different causal mechanisms, the article concludes that socioeconomic inequality, by making elections endogenous to preexisting social power, can be a major and underappreciated barrier to the long-term process of democratization even after the “choice” of formally democratic rules.
Journal Article
VAT fraud and reverse charge: empirical evidence from VAT return data
In order to stop Value-Added Tax (VAT) fraud, EU member states use the so-called reverse-charge (RC) mechanism, which effectively removes VAT withholding and refunding in business-to-business transactions. Using the German VAT return data, we examine the effects of the introduction of RC and find that requests of input tax refunding decline sharply in the affected industries, supporting the presence of fraud prior to the introduction of RC. Based on our estimates, we quantify the revenue losses from VAT fraud prior to RC implementation in these industries to be around 5% of VAT revenues.
Journal Article
Application of Artificial Intelligence for Fraudulent Banking Operations Recognition
This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.
Journal Article
Population dynamics of welfare stigma: welfare fraud versus incomplete take-up
This study investigates the conditions under which welfare fraud and incomplete take-up emerge simultaneously and persist for a long time, which has been observed in many countries, particularly Japan and Germany. To do this, we extend models of statistical discrimination and taxpayers’ resentment to simple models of population dynamics. We find two stable boundary equilibria in the first model. One of these equilibria entails low welfare fraud and
100
%
incomplete take-up, and the other entails high welfare fraud and
100
%
take-up. In contrast, we find a unique stable equilibrium in the tax resentment model, which is interior and thus allows for the coexistence of welfare fraud and incomplete take-up in a long run. Hence, we conclude that this unique long-run equilibrium of the dynamic taxpayers’ resentment model provides a better explanation for the observation of simultaneous and persistent presence of welfare fraud and incomplete take-up in actual economies.
Journal Article
Towards a Systematic Screening Tool for Quality Assurance and Semiautomatic Fraud Detection for Images in the Life Sciences
The quality and authenticity of images is essential for data presentation, especially in the life sciences. Questionable images may often be a first indicator for questionable results, too. Therefore, a tool that uses mathematical methods to detect suspicious images in large image archives can be a helpful instrument to improve quality assurance in publications. As a first step towards a systematic screening tool, especially for journal editors and other staff members who are responsible for quality assurance, such as laboratory supervisors, we propose a basic classification of image manipulation. Based on this classification, we developed and explored some simple algorithms to detect copied areas in images. Using an artificial image and two examples of previously published modified images, we apply quantitative methods such as pixel-wise comparison, a nearest neighbor and a variance algorithm to detect copied-and-pasted areas or duplicated images. We show that our algorithms are able to detect some simple types of image alteration, such as copying and pasting background areas. The variance algorithm detects not only identical, but also very similar areas that differ only by brightness. Further types could, in principle, be implemented in a standardized scanning routine. We detected the copied areas in a proven case of image manipulation in Germany and showed the similarity of two images in a retracted paper from the Kato labs, which has been widely discussed on sites such as pubpeer and retraction watch.
Journal Article
The science institutions hiring integrity inspectors to vet their papers
Some researchers have their manuscripts screened for errors before they go to journals.
Some researchers have their manuscripts screened for errors before they go to journals.
Journal Article
Exploring the Intersection of Machine Learning and Big Data: A Survey
The integration of machine learning (ML) with big data has revolutionized industries by enabling the extraction of valuable insights from vast and complex datasets. This convergence has fueled advancements in various fields, leading to the development of sophisticated models capable of addressing complicated problems. However, the application of ML in big data environments presents significant challenges, including issues related to scalability, data quality, model interpretability, privacy, and the handling of diverse and high-velocity data. This survey provides a comprehensive overview of the current state of ML applications in big data, systematically identifying the key challenges and recent advancements in the field. By critically analyzing existing methodologies, this paper highlights the gaps in current research and proposes future directions for the development of scalable, interpretable, and privacy-preserving ML techniques. Additionally, this survey addresses the ethical and societal implications of ML in big data, emphasizing the need for responsible and equitable approaches to harnessing these technologies. The insights presented in this paper aim to guide future research and contribute to the ongoing discourse on the responsible integration of ML and big data.
Journal Article