Legal Aspects of “White-Label” Banking in the European, Polish and German Law
Bibliographic record
Abstract
Offering “White-label” products and services is a well-developed business sector in the European market. At present, this market concept is also increasingly being applied to financial services, as part of a bank–FinTech cooperation. A question arises, however, as to the proper place for such models within the complex system of European financial law. This article reviews the “White-label” frameworks currently operating in the banking sector and the corresponding regulations of the European Union law, based on their application in German and Polish legal system. Purposive, grammatical, and comparative law methods were used to study the content of legal acts. As a result, the principles of two primary models of White-label banking were established. The first model is based on a bank acting only as an outsourcing service provider. In the second model, a bank also operates on the basis of a license it was granted. Both models have a common legal origin in European Union law, but local variations exist depending on the legal system of a given Member State.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".