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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".