Risks, Regulations, and Impacts of <scp>FinTech</scp> Adoption on Commercial Banks in the United States and Canada: A Comparative Analysis
Bibliographic record
Abstract
ABSTRACT The growth of FinTech has increasingly attracted the attention of financial industry players. The speed and complexity with which new financial technologies have spread around the world have created regulatory challenges for the United States and Canadian authorities. Unlike the United States, where there is regulatory fragmentation and a supervisory instrument with regulatory relief, Canada has a more integrated regulatory approach managed by regulatory sandbox principles. The aim of this article is to study and compare the impact of FinTech adoption on commercial banks over the 2018–2023 period in the United States and Canadian FinTech ecosystems. The results essentially show that FinTech has a positive impact on commercial banks' performance and financial growth, and a negative impact on liquidity and financial risk. Our results present several contributions and mainly show that: (1) In the presence of multiple entry channels for FinTech startups, the impact of FinTech on financial performance is higher in the United States than in Canada. (2) Because of its regulatory approach, Canada lags behind the United States in the adoption rate of financial technologies. (3) Balance of power in the financial sector induces commercial banks not to consider FinTech startups as threats but rather as partners that offer opportunities for expertise and reduced regulatory costs. Finally, FinTech can be disruptive and presents many challenges for regulators given the complexity and speed of innovation it promotes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".