Regulating Fintech in Canada and the United States: Comparison, Challenges and Opportunities
Bibliographic record
Abstract
This article compares the regulatory frameworks, challenges and opportunities in financial technology (fintech) in Canada and the United States. Fintech is explored as a post 2008 global financial crisis phenomenon by reviewing the diverse interpretations of its definition, identifying its historical underpinnings, and noting industry trends and associated demand factors. The market environment, and regulatory approach, in Canada and the U.S. is not homogenous, and although there are similarities, each jurisdiction faces different challenges and opportunities. In the U.S., fintech has significant disintermediation potential, and supervisory structures exhibit fragmentation under a rules-based framework. Nevertheless, there is growing desire for principle-based regimes in the U.S., and several federal and state regulators have instituted “regulatory sandboxes.” Canada is characterized by principle-based regulation and features a robust sandbox in securities jurisdiction; yet fintech is largely being experienced as a bank-driven phenomenon, and bank-fintech partnerships are visible, as incumbents use fintech to enhance customer service and operations. Regulatory fragmentation does, however, present an entry cost for new consumer-facing firms in Canadian fintech sectors not falling within the ambit of federal financial institution oversight. Similarities and differences between the U.S. and Canada are explored in this article across multiple fintech sectors including fintech banking, cryptocurrency (cryptocurrency as money, cryptocurrency funds and derivatives, and initial coin offerings), fintech credit (peer-to-peer lending), payments, algorithmic wealth management (robo-advisors) and financial account aggregators. The article also discusses regulatory adaptations such as sandboxes; the status of large-scale financial blockchain implementation projects; the emergence of "regtech"; international regulatory coordination efforts; self-regulatory structures; systemic risk considerations; and optimal regulatory design principles, given continuing market and product complexity. Fintech presents opportunities - like lower costs, enhanced product and service scope, greater credit and financial inclusion – and unique new risks (which are explored in detail in the article) as well as challenges for regulators, such as creating laws that accurately capture new technology and keeping pace with constantly evolving innovations. Regulators must also balance encouraging innovation and competition with effective risk management and supervision.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".