Artificial Intelligence and Big Data in Fraud Analytics:Identifying the Main Data Protection Challenges for Public Administrations

Fraud Analytics refers to the use of Big Data Analytics to detect fraud. Numerous techniques, from data mining to social network analysis, are applied to detect various types of fraud. While Fraud Analytics offers the promise of more efficiency in fighting fraud, it also raises data protection challenges for public administrations. Indeed, whether they use traditional or advanced techniques, administrations consistently use more and more data to deliver public services. In this regard, they often need to process citizen’s personal data. Therefore, administrations have to consider data protection legal requirements. While these legal requirements are well documented, the concrete way in which they have been integrated by public administrations in their Fraud Analytics process remains unexplored. Accordingly, we examine two case studies within the Belgian Federal administration (the detection of tax frauds and of social security infringements), in order to shed light on the main data protection challenges faced by public administrations in this regard

Corporate Criminal Liability: An Overview of Federal Law

[Excerpt] Under federal law, corporations or most other legal entities may be criminally liable for the crimes of their employees and agents. This is true in the case of regulatory offenses, like crimes in violation of the Federal Food, Drug, and Cosmetic Act; it is true in the case of economic offenses, like crimes in violation of the securities laws; and it is true in the case of common law crimes, like keeping a house of prostitution in violation of the Mann Act. Ordinarily, the agents and employees who commit the crimes for which their principals and employers are liable also face prosecution and punishment. Individual criminal statutes, Justice Department policies, and the Sentencing Guidelines largely dictate the circumstances under which, and the extent to which, agents, employees, corporations, and similar unincorporated entities are prosecuted and punished. This is a brief overview of federal law in the area

Do Fraudulent Companies Employ Different Linguistic Features in Their Annual Reports? An Empirical Study Using Logistic Regression and Random Forest Methodologies

The use of textual analysis to uncover fraudulent actions in 10-K filings is widespread. The previous studies have looked at the Management Disclosure and Analysis (MD&A) section of annual reports to predict illicit behaviour by analysing the tone of executives, with the majority of those studies dating back 10 years or more. The primary goal of this research is to find patterns in linguistic features of entire annual reports of convicted public businesses, which were found using the Corporate Prosecution Registry database, and compare them to non-fraudulent equivalents in the same industry. The algorithms of logistic regression and random forest are implemented to discover important factors and make accurate predictions. The accuracy rate, ROC-AUC value, and 10-fold cross-validation tools are performed to validate the success of each method. The results of the logistic regression revealed that corrupt organisations utilise a more negative, uncertain, and litigious tone. Furthermore, these businesses employ more words with a high lexical diversity and minimal complexity. Based on the Random Forest machine learning technique, the litigious variable is the most important variable in the prediction of untruthful corporations. Moreover, each of the validation methods demonstrates that the Random Forest methodology outperforms logistic regression.nhhma

Criminal Lying, Prosecutorial Power, and Social Meaning

This article concerns the prosecution of defensive dishonesty in the course of federal investigations. It sketches a conceptual framework for violations of 18 U.S.C. § 1001 and related false-statement charges, distinguishes between harmful deception and the typical investigative interaction, and describes the range of lies that fall within the wide margins of the offense. It then places these cases in a socio-legal context, suggesting that some false-statement charges function as penalties for defendants’ refusal to expedite investigations into their own wrongdoing. In those instances, the government positions itself as the victim of the lying offense and reasserts its authority through prosecution. Enforcement decisions in marginal criminal lying cases are driven by efficiency rather than accuracy goals, which may produce unintended consequences. Using false-statement charges as pretexts for other harms can diminish transparency and mute signals to comply. Accountability also suffers when prosecutors can effectively create offenses, and when it is the interaction with the government itself rather than conduct with freestanding illegality that forms the core violation. The disjunction between prosecutions and social norms about defensive dishonesty may also result in significant credibility costs and cause some erosion of voluntary compliance. Animating the materiality requirement in the statute with attention to the harm caused or risked by particular false statements could mitigate these distortions. An inquiry into the objective impact of a false statement might account for the nature of the underlying conduct under investigation, whether the questioning at issue is pretextual, whether the lie is induced, and whether the deception succeeds or could succeed in harming the investigation. By taking materiality seriously, courts could curtail prosecutorial discretion and narrow application of the statute to cases where prosecution harmonizes with social norms