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\"Illicit work, social security fraud, economic crime, and other shadow economy activities are fast becoming an international problem. This second edition uses new data to reassess currency demand and the model approach to estimate the size of the shadow economy in seventy-six developing, transition, and OECD countries. This updated edition argues that during the 2000s the average size of a shadow economy varied from 19% of GDP for OECD, to 30% for transition, and to 45% for developing countries\"-- Provided by publisher.

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 paper examines the relation between corporate lobbying and fraud detection. Using data on corporate lobbying expenses between 1998 and 2004, and a sample of large frauds detected during the same period, we find that firms’ lobbying activities make a significant difference in fraud detection: Compared to nonlobbying firms, on average, firms that lobby have a significantly lower hazard rate of being detected for fraud, evade fraud detection 117 days longer, and are 38% less likely to be detected by regulators. In addition, fraudulent firms on average spend 77% more on lobbying than nonfraudulent firms, and they spend 29% more on lobbying during their fraudulent periods than during nonfraudulent periods. The delay in detection leads to a greater distortion in resource allocation during fraudulent periods. It also allows managers to sell more of their shares.

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.

The primary objective of this study is to uncover and examine the patterns of scientific collaboration within the domain of tax evasion and tax avoidance spanning the years 1975 to 2022. To analyze the dissemination of knowledge on a worldwide scale, we investigated the interconnections among authors, journals, countries, and institutions. A total of 1456 publications were retrieved from the Web of Science repository. Bibliographic analysis and network visualization were conducted using CiteSpace. The publications analysed in this study consisted of 1,456 articles authored by 2478 scholars affiliated with 1355 institutions. The publications were distributed among 724 distinct journals and originated from 98 countries. The United States of America is found to be the most productive nation, with McGee, R.W. being recognised as the most prolific author. The League of European Research Universities is recognised as the most productive institution, whereas the Journal of Public Economics is identified as the most productive publication. The findings show that authors who exhibit high levels of productivity also tend to demonstrate a strong inclination toward collaboration. Furthermore, the findings reveal that the interest of the scholars in particular topics in this research has evolved over time.

Detecting tax fraud is a top objective for practically all tax agencies in order to maximize revenues and maintain a high level of compliance. Data mining, machine learning, and other approaches such as traditional random auditing have been used in many studies to deal with tax fraud. The goal of this study is to use Artificial Neural Networks to identify factors of tax fraud in income tax data. The results show that Artificial Neural Networks perform well in identifying tax fraud with an accuracy of 92%, a precision of 85%, a recall score of 99%, and an AUC-ROC of 95%. All businesses, either cross-border or domestic, the period of the business, small businesses, and corporate businesses, are among the factors identified by the model to be more relevant to income tax fraud detection. This study is consistent with the previous closely related work in terms of features related to tax fraud where it covered all tax types together using different machine learning models. To the best of our knowledge, this study is the first to use Artificial Neural Networks to detect income tax fraud in Rwanda by comparing different parameters such as layers, batch size, and epochs and choosing the optimal ones that give better accuracy than others. For this study, a simple model with no hidden layers, softsign activation function performs better. The evidence from this study will help auditors in understanding the factors that contribute to income tax fraud which will reduce the audit time and cost, as well as recover money foregone in income tax fraud.

Tax evasion operates beyond the boundaries of the jurisdictions, it develops across borders, and the extent of tax fraud as a phenomenon is differentiated according to the aspects and the rigours of legislation, as well as according to the economic environment of each country in part. Regardless of the level of development of the country in which it manifests, the effects of the fiscal fraud are destructive, affecting both the state budget, as well as the financial resources of the offenders’ commercial partners. The fiscal fraud has negative effects over the economic market, and one of the consequences is the social inequality from the perspective of the abuse and the predisposition of certain taxable subjects to fraud. Certainly, the phenomenon remains unraveled, the financial schemes being extremely complex, and the fraud mechanisms are some of the most laborious ones and in a permanent improvement, while the evaders keep finding new means by way of which they illegally attract financial resources. The fight against tax evaders is a difficult one, mainly because of the sometimes “deliberate” legislative “loopholes”, but also because of their ingenuity, which is often “one step” ahead of those who fight against tax evasion.

The goal of the present research is to contribute to the detection of tax fraud concerning personal income tax returns (IRPF, in Spanish) filed in Spain, through the use of Machine Learning advanced predictive tools, by applying Multilayer Perceptron neural network (MLP) models. The possibilities springing from these techniques have been applied to a broad range of personal income return data supplied by the Institute of Fiscal Studies (IEF). The use of the neural networks enabled taxpayer segmentation as well as calculation of the probability concerning an individual taxpayer’s propensity to attempt to evade taxes. The results showed that the selected model has an efficiency rate of 84.3%, implying an improvement in relation to other models utilized in tax fraud detection. The proposal can be generalized to quantify an individual’s propensity to commit fraud with regards to other kinds of taxes. These models will support tax offices to help them arrive at the best decisions regarding action plans to combat tax fraud.

Credence goods are characterized by informational asymmetries between sellers and consumers that invite fraudulent behaviour by sellers. This article presents a natural field experiment on taxi rides in Athens, Greece, set up to measure different types of fraud and to examine the influence of passengers' presumed information and income on the extent of fraud. We find that passengers with inferior information about optimal routes are taken on significantly longer detours, while lack of information on the local tariff system increases the likelihood of manipulated bills by about fifteen percentage points. Passengers' perceived income seems to have no effect on fraud.

Objective/Context: This article explains the causes of the tax evasion on metals in the royal mines of the province of Pamplona in the New Kingdom of Granada (nrg), in which royal box metals were not declared from the year 1636 to 1678. However, there were miners, mayors of mines, indigenous and enslaved people dedicated to mining in that same period. The Madrid authorities sought to establish the amounts defrauded through two visits, one carried out in 1659 and the other in 1676. Methodology: In addition to fiscal, ordinary and general visits, were consulted accounts of the royal treasury of Pamplona and the historiography of the New Granada mining. Originality: These revisions made it possible to show how the tax on metals was cheated in this jurisdiction and offer answers to a widespread phenomenon throughout Latin America and evoked by historians but rarely exposed in its details and specific cases, given its nature and the scant documentary footprint. Conclusions: Among the results, it is highlighted that the values potentially defrauded in the years analyzed, 1636-1678, reflect a higher production than that given in the stage with tax footprint: 1617-1635. It was also found that authorities from the Audiencia de Santa Fe and the Pamplona council participated in the fraud; it was a matter socially shared between miners and authorities. Objetivo/Contexto: este artículo examina las causas de la evasión del impuesto a los metales en los reales de minas de la provincia de Pamplona del Nuevo Reino de Granada (nrg), en cuya Caja Real no se declararon metales desde el año 1636 hasta 1678, si bien en ese mismo periodo hubo mineros, alcaldes de minas, indios y esclavos dedicados a la minería. Las autoridades de Madrid buscaron establecer los montos defraudados mediante dos visitas, una llevada a cabo en 1659 y otra, en 1676. Metodología: además de visitas fiscales, ordinarias y generales, se consultaron cuentas de la Caja Real de Pamplona y la historiografía sobre la minería neogranadina. Originalidad: la revisión documental permitió mostrar la forma como se evadía el impuesto a los metales en esta jurisdicción y ofrecer respuestas a un fenómeno generalizado en toda Hispanoamérica y evocado por los historiadores, pero pocas veces expuesto en sus detalles y en casos concretos, dada su naturaleza y la escasa huella documental. Conclusiones: entre los resultados se destaca que los valores potencialmente defraudados en los años analizados, 1636-1678, reflejan una producción mayor que la dada en la etapa con huella tributaria: 1617-1635. También se constató que en el fraude participaban funcionarios de la Audiencia de Santa Fe y del cabildo de Pamplona, es decir, se trataba de un asunto socialmente compartido entre mineros y autoridades. Objetivo/contexto: neste artigo, são examinadas as causas da sonegação do imposto dos metais nos reais de minas da província de Pamplona do Novo Reino de Granada (nrg), em cuja Caixa Real não foram declarados metais de 1636 a 1678, apesar de, nesse mesmo período, ter havido mineradores, alcaide de minas, indígenas e escravos dedicados à mineração. As autoridades de Madri procuraram estabelecer os montantes defraudados mediante duas visitas, uma realizada em 1659 e outra em 1676. Metodologia: além de visitas fiscais, ordinárias e gerais, foram consultadas contas da Caixa Real de Pamplona e a historiografia sobre a mineração neogranadina. Originalidade: a revisão documental permitiu demonstrar a forma como se sonegava o imposto dos metais nessa jurisdição e oferecer respostas a um fenômeno generalizado em toda a América Hispânica e evocado pelos historiadores, mas poucas vezes exposto com detalhes e em casos concretos, tendo em vista sua natureza e o escasso rastro documental. Conclusões: entre os resultados, destaca-se que os valores potencialmente defraudados nos anos analisados, 1636-1678, refletem uma produção maior do que a dada na etapa com indícios tributários, 1617-1635. Também se constatou que, da fraude, participavam funcionários da Audiência de Santa Fé e do cabido de Pamplona, isto é, tratava-se de um assunto socialmente compartilhado entre mineradores e autoridades.