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Cross-Modal Multivariate Pattern Analysis
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Predicting Fraud Victimization Using Classical Machine Learning
1Faculty of Management, Royal Roads University, Victoria, BC V9B 5Y2, Canada.
Entropy (Basel, Switzerland)
|April 3, 2021
Summary
This study identifies key demographic traits of likely investment fraud victims in Canada. Female investors with poor financial knowledge and retired individuals are more vulnerable to fraud.
Area of Science:
- * Financial Consumer Protection
- * Behavioral Economics
- * Machine Learning in Finance
Background:
- * Investment fraud poses a persistent threat to Canadian financial consumers.
- * Understanding victim demographics is crucial for effective fraud prevention strategies.
Purpose of the Study:
- * To predict the demographic characteristics of investors susceptible to investment fraud.
- * To identify factors influencing the likelihood of becoming an investment fraud victim.
Main Methods:
- * Utilized a machine-learning algorithm to analyze a dataset of 4575 fraud victims.
- * Data sourced from the Investment Industry Regulatory Organization of Canada (IIROC) database (2009-2019).
Main Results:
- * Identified typical fraud victims as female, possessing poor financial knowledge, knowing their advisor previously, and being retired.
- * Limited financial literacy combined with a long-standing advisor relationship reduces victimization risk.
- * Male investors with low/moderate investment knowledge are at higher risk; older adults show a non-significant increased risk.
Conclusions:
- * Findings offer actionable insights for Canadian regulatory bodies to enhance investor protection mandates.
- * The study highlights the need for targeted financial literacy programs and advisor relationship oversight.
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