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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".