The morality and tax avoidance: A sentiment and position taking analysis.

This paper examines the moral and legal underpinnings of corporate tax avoidance. Cast in terms of a totemic symbol that brand tax avoidance as within the purview of the law, the paper invokes the attributional frames of the new sociology of morality to examine the position of both the moral advocates and the amoral critics of aggressive tax avoidance. The paper uses the United Kingdom as a jurisdiction where complex tax planning by tax advisors serves as a measure of protection for corporations who may have already conceived that they are paying too much tax. Data for the paper came from semi-structured interviews conducted with tax accountants, consultants, parliamentarians, and government officials. To supplement the interviews, data from the Parliamentary Commission on Banking Standards were collected and analyzed to provide useful insights. The findings reveal that through effective tax planning, companies can reduce the present values of future tax payments. Given the singular justification of their actions within the contours of the tax rules, the moral culpability of organized tax avoidance is minimized, with very little liability attached. Tax avoidance is a morally charged area that is slowly drifting away from conventional social norms of what is right or wrong. It is hard not to see those in charge of tax regulation not using the findings of this paper to provide a more nuanced understanding of the intractable problems associated with corporate tax avoidance and use it as a reference point for regulatory reforms.

The determinants of investment fraud: A machine learning and artificial intelligence approach.

Investment fraud continues to be a severe problem in the Canadian securities industry. This paper aims to employ machine learning algorithms and artificial neural networks (ANN) to predict investment in Canada. Data for this study comes from cases heard by the Investment Industry Regulatory Organization of Canada (IIROC) between June 2008 and December 2019. In total, 406 cases were collected and coded for further analysis. After data cleaning and pre-processing, a total of 385 cases were coded for further analysis. The machine learning algorithms and artificial neural networks were able to predict investment fraud with very good results. In terms of standardized coefficient, the top five features in predicting fraud are offender experience, retired investors, the amount of money lost, the amount of money invested, and the investors' net worth. Machine learning and artificial intelligence have a pivotal role in regulation because they can identify the risks associated with fraud by learning from the data they ingest to survey past practices and come up with the best possible responses to predict fraud. If used correctly, machine learning in the form of regulatory technology can equip regulators with the tools to take corrective actions and make compliance more efficient to safeguard the markets and protect investors from unethical investment advisors.

Predicting Fraud Victimization Using Classical Machine Learning.

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada's (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors' protection mandates.