Regulating Fintech in Canada and the United States: Comparison, Challenges and Opportunities
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».