The demographic profile of victims of investment fraud: an update
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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. Design/methodology/approach In all 235 cases disclosing the details of financial crime victims are collected from the Industry Regulatory Organization of Canada (IIROC) enforcement platform between June of 2009 and December of 2019 for the analysis. The study used a descriptive analysis to showcase the demographic characteristics of investors who have been victims of financial crimes in Canada. Findings The findings indicate that these investors of age 60 and above were more likely to fall prey to various types of financial crime. The results also disclosed that retirees and investors with limited investment knowledge increase the probability of being vulnerable to the perpetrators than others. Research limitations/implications Overall, the study helps regulators in the securities industry gain insights into demographic portraits of the more vulnerable investors. Hence, more precautionary measures could pitch into these concerns to protect specific subsets of investors from investment fraud. Originality/value Individuals who are more vulnerable to investment fraud might not be entirely comparable with the stereotypical victims that most studies portray. The research gap could cause individual investors who appear to be at lower risk to unconsciously fall prey to investment fraud. The IIROC study, detailing the demographic factors of victims, can fill the gap and improve understanding of the tendency of victims.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it