Financial Exploitation in Canada: A Predictive Model using ML and AI

Abstract

The story of financial victimization and abuse of the elderly, women, and migrants is not new to Canada. However, its implications were given serious legal and social consideration only a decade back when reports on financial exploitation were published by Vancity Credit Union as “The Invisible Crime” in 2014 and as “Suffering in Silence” in 2017. These research studies pointed out that only 3% of the elderly population in Canada were aware of their financial abuse but there were at least 36% of the population that were not aware of being defrauded. National Survey on the Mistreatment of Older Canadians by the University of Toronto in 2015 estimated that by 2030, this crime would target at least 70% of the elderly and vulnerable population of Canada. Hence was the need for a model that could predict financial fraud, prevent victimization, and was practice friendly. The review of literature pointed to a mix of personal, interpersonal, and institutional factors that increased the attractiveness of victims and the capacity of offenders. The present study uses machine learning and neural networks to develop a logistic regression model that could predict financial exploitation in Canada. Using the data from the Investment Industry Regulatory Organization of Canada, a predictive model based on the three highest predictors of financial exploitation in Canada-age, income, and total net worth was developed. It is difficult to imagine regulators in Canada not using the findings from this study to inform policies regarding financial regulation and victims' protection. This study is also among the few studies that make a deep dive into the Canadian context and people making the prediction model available to policyholders and financial institutions for thwarting the attempts of defrauding the vulnerable sections of society in Canada notwithstanding the extension of the model to other economies.