Adopting Open Banking in Canada: An Analysis of Current Global Frameworks
Bibliographic record
Abstract
Financial technology or fintech is a rapidly evolving and disruptive development within the financial sector. New technologies arising from fintech innovations are changing how consumers access and utilize financial services while also allowing new fintech firms to compete within the financial sector. As fintech innovations proliferate throughout the financial sector benefits are being seen globally from growing financial inclusion, increased access to capital for small and medium enterprises and improved operational efficiencies for financial firms. Developments from fintech innovations do come with potential drawbacks that regulators are working to address. Fintech has the potential to adversely affect banking sector stability and presents increased cyber-attack risks that adversely affect firms and consumers, requiring regulators to adapt in response to these issues. One regulatory response that has gained traction globally for its ability to harness fintech competition and innovation while maintaining a more secure and stable financial system is that of open banking. Open banking is a regulatory approach that allows consumers to opt in and opt out of sharing their personal financial data with financial firms. Open banking also creates opportunities for more secure transfers of personal financial data by discouraging a data collection practice known as ‘screen-scrapping’, currently utilized by fintech firms, by allowing them to collect personal financial data through more secure application programming interfaces (API). With the permission of the consumer open banking frameworks usually obligate firms to transfer personal financial data via API to another firm who then utilize that data to develop consumer-centric products for customers. Canada, praised globally for the security and stability of its financial system, has been slow in developing an open banking framework and is now at risk of being left behind jurisdictions that have chosen to harness the competition, cyber-security and innovation advantages open banking can bring. To aid Canada in developing an effective open banking framework this capstone examines the regulatory approaches to open banking that have been developed in several jurisdictions including the U.S. and EU. A comparative analysis is conducted to identify what aspects of current regulatory approaches to open banking Canada can utilize in developing its own successful open banking framework. It is concluded that Canada should build on the strengths of its secure and stable financial system to become a ‘fast follower’ in developing an open banking framework that will enable it to attract further fintech investment and transform itself into a global leader in fintech innovation. Based on the comparative analysis of open banking frameworks several jurisdictions have developed the following recommendations are made to ensure Canada’s prospective open banking framework will enable it to become a global fintech leader:
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.022 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".