Banks and Fintechs: How to Develop a Digital Open Banking Approach for the Bank’s Future
Bibliographic record
Abstract
Mutated market conditions, the advent of new players and digital technologies, and a significant regulatory push, are profoundly changing the banking industry. Banking business models may shift significantly from a pipeline, vertical, paradigm, to open banking models where modularity can be an opportunity for banks. Not only are the abovementioned factors representing a threat to the traditional model, but also they are spurring significant new opportunities to pursue new revenue streams. Those opportunities are exploited through new banking paradigms that entail higher levels of openness towards third parties and a crescent number of modular services bundled together. Models can go to mere compliance with the prescriptions of openness of PSD2, to the inclusion of new services, the opening of the banking core and data, and the aggregation of those within a platform experience. Value is created in platforms through economies of scope in production and innovation.This paper has explored the evolution of Fintech and Techfin in the market and the emergence of platform models in banking. It has investigated the evolution of that concept, also introducing an interesting banking case (BBVA), which gives several insights on the choices made toward a Banking-as-a-Platform model within the context of Fintech and Open Banking.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".