Why are open banking models in Europe underperforming?
Bibliographic record
Abstract
This study investigates the foundations underpinning open banking models in Europe and identifies levers to improve their performance. Based on a review of the literature, it distinguishes four contexts for open banking: platformisation, data sharing, FinTech and regulation. The users of open banking services are surveyed to determine factors driving adoption and identify those entities that customers trust with their data and funds. The results indicate that the slow adoption of open banking services is in large part due to customers’ poor understanding of such services. The results also show the importance of usefulness and trust in driving adoption. These findings highlight the disproportionate attention being given to service provider infrastructure and the ecosystems of new entrants, and indicate that more consideration should be given to the actual users of open banking frameworks. In response to the findings, the study proposes a roadmap to mitigate the main weaknesses in current open banking models. The conclusions of this study are relevant not only to the development of open banking regulations in other territories, such as the USA and Canada, but also to the extension of data-sharing regulations to non-banking sectors.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".