Reducing Financial Debt and Illiteracy in Canadian Populations Using Machine Learning Prediction Models
Bibliographic record
Abstract
In this paper, we conducted a study on Financial Recommender Systems using tailored prediction and recommendation models. As financial debt and illiteracy rise across Canada, especially among Generation Z, there is a growing need for intelligent and accessible financial guidance. To address this, we synthesize a realistic dataset of 100 simulated Canadian users, we model financial behaviour using demographics, savings, debt, and expense data. Our system integrates multiple components: Isolation Forest for anomaly detection, ARIMA and LSTM for expense forecasting and a rule-based recommendation system (RecSys) to generate personalized financial advice. These elements form a unified pipeline that identifies abnormal spending, forecasts future expenses and delivers risk-aligned recommendations. Experimental results show that our hybrid forecasting model effectively predicts future financial behaviour, while the recommendation engine generates user-specific plans with heuristic scoring for priority labelling. Although this paper uses synthetic data and a rule-based RecSys, it provides proof of concept for scalable, practical applications. Future work includes real-user testing, dynamic LLM integration with an API key, and full-stack deployment.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".