Front. Big Data
Sec. Machine Learning and Artificial Intelligence
doi: 10.3389/fdata.2022.961039

The determinants of Investment Fraud: A Machine Learning and Artificial Intelligence Approach

  • 1Royal Roads University, Canada
Provisionally accepted:
The final, formatted version of the article will be published soon.

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.

Keywords: Investment fraud, machine learning, Artifici al Intelligence, Self-regulation, Regulatory technology

Received: 03 Jun 2022; Accepted: 31 Aug 2022.

Copyright: © 2022 Lokanan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Mark Lokanan, Royal Roads University, Victoria, Canada