Virtual IBAN as a Service in the Law of the European Union and Poland
Bibliographic record
Abstract
The purpose of this paper is to present the two existing virtual account models functioning in the European Union, examine their legal validity and identify the legal challenges related to the functioning of these models. The first model, Mass Payment Accounts, which is related to virtual accounts rather than to virtual IBANs, is the model where the licensed financial institution only provides a business payment (settlement) account, with technical subaccounts, to one of their business clients. The functionality of the subaccounts is limited to reflect and distinguish the incoming payments. The second and more complex model is the vIBAN solution, where the licensed payment institution provides, to another licensed financial institution, indirect access to local payment schemes (hereinafter referred to as “vIBAN”). To confirm the legal validity and identify the potential risks of vIBAN services, EU law was analysed with some insights from Polish law. The reason for introducing vIBAN services is the difficulty for certain payment service providers to participate in so-called designated payment systems. Designated payment systems are usually the most widespread local payment systems. The reason for the different treatment of these designated systems is banking systemic risk, understood as a situation where a default by a system participant may result in a default by other participants. Consequently, even if a given payment service provider can obtain its own IBAN number, there is often no possibility for it to participate in designated payment schemes. Bearing in mind the different rules in the case of designated payment systems, the legality of vIBAN services in the EU law is justified by the principle of free movement of services, the principle of equal access to payment schemes and the obligation of the credit institutions to provide banking and non-banking participants with credit institution payment account services on an objective, non-discriminatory and proportionate basis. However, there are various challenges related to the functioning of vIBAN services, such as the overlapping of certain AML/CFT obligations, enforcement of administrative and court seizures, AML-related blocking of vIBANs and consistency of money transfer sender data with the Fund Transfer Regulation. The most pressing challenges requiring prompt regulation on the European level are related to the applicable deposit protection scheme, as well as to specific Member States’ administrative restrictions, which can cause difficulties in offering vIBAN services to business entities.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".