Financial Inclusion in British Columbia: Evaluating the Role of Fintech
Bibliographic record
Abstract
Financial technology (fintech) can help to mitigate the problem of financial exclusion in British Columbia. Individuals who participate in the traditional banking and financial system experience a variety of social and economic benefits. Yet several factors like personal hardship, financial illiteracy, high product costs, perceived eligibility, informational gaps, a lack of credit history and legal documents, bank resistance, and customer feelings of distrust and disrespect contribute to the exclusion of many from traditional financial products and services. People who are “unbanked” and “underbanked” often turn to high-cost (even predatory) substitutes like payday lenders, rent-to-own firms, cheque-cashing services, and pawn shops. This paper illustrates how some fintech innovations—highlighting numerous companies operating in British Columbia, Canada, and internationally—can benefit people who are unbanked and underbanked as an alternative to “fringe” banking. Fintech is not, however, a panacea for those excluded, marginalized, or underserved by traditional financial firms, and there are several implementation barriers and integration risks in this market development. This paper provides seven key policy recommendations to help maximize the inclusionary benefits of fintech in British Columbia while mitigating its potential risks.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".