The paper contributes to a growing body of empirical work on regulatory technology by
proposing machine learning models to detect fraud in financial markets. The recent spate of …

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 …

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 …

This paper aims to classify disciplinary hearings into two types (settlement and contested).
The objective is to employ binary machine learning classifier algorithms to predict the …

Purpose This study aims to examine the demographic factors of investors, contributing to
financial victimization that occurs in Canada from June of 2008 to December of 2019 …

Purpose This paper aims to analyze the processing of complaints against investment
advisors and Member firms through the Investment Industry Regulatory Organization of …

This study employed Stilger's (1971) capture theory to investigate whether the Investment
Dealers Association (IDA) of Canada experienced regulatory capture by weak enforcement …

Purpose This paper aims to examine the impact of public scrutiny on chief executive officer
(CEO) compensation at Standard & Poor's (S&P) 500 firms. Design/methodology/approach …

The paper argues for a restorative justice approach to corporate fraud. Drawing from the
restorative justice and crime control literature, the paper argues that given the nature of …

This study reviews trends and prevention strategies for mortgage income fraud in Canada's
housing market. It examines fraudulent practices in mortgage applications, assesses …