The Datafication of Open Banking: A critical interrogation into the data privacy issues and cybersecurity risk implications of cross-border data flows under Canada’s proposed Open Banking Framework
Bibliographic record
Abstract
Banks have always served as the chief custodians of financial data and in this role, they regulate the activities between customers, technology, and merchants. The worldwide consumer demands have added pressure to financial institutions to adopt more streamlined methods when it comes to accessing financial data. This comes at a time when the financial services industry sits on the verge of pending reforms through digitisation and crossborder transactions. Open banking is one such change which is predicted to shake up the traditional banking model and is expected to bring a plethora of benefits to both customers and the financial industry. Open banking provides access to consumer banking, transactions, and other financial information to third party providers (TPPs) via application programming interfaces (APIs). Open banking has the possibility of expanding to include user consent-based movement of information for investments, insurance, telecommunications, utilities and more. This ability to share financial data through APIs could promote faster, easier, and more secure payments, particularly crossborder transactions. There are three major challenges with open banking that this research covers. The first is that open banking introduces a consumer data portability feature at a time when there is no existing right under the current law. The second is that open banking is a consent-based system that will require a higher standard of consent from a privacy law perspective especially in relation to crossborder transactions. The third is that open banking exacerbates existing cybersecurity risks while creating new ones which may require additional protections through either the financial or privacy law regimes. It is useful to explore that each country imposes separate regulatory limits on what personal data can be transferred or stored in their markets and whether there can ultimately be interoperability of these structures for crossborder transactions. Open banking raises concern that it may become a dangerous route for criminals to trick naïve consumers into disclosing secret information, allowing illegal access to their personal data. As such, there is no room for error in rolling out open banking as a model, as its failure could result in harsh economic impacts across the financial sector.
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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 | 0.037 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.019 | 0.047 |
| Scholarly communication | 0.038 | 0.025 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.013 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".