Financial Exploitation in Canada: A Predictive Model using ML and AI
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".