Central Bank Digital Currencies for Cross-border Payments
Bibliographic record
Abstract
Over the years, the demand for seamless and inexpensive cross-border payments has grown in parallel with growth in international e-commerce, remittances and tourism. Yet, cross-border payments have not kept pace with the intensive modernization that has characterized domestic payment services worldwide. An alternative avenue to modernize delivery of cross-border payment services is being increasingly explored in the context of central banks issuing their own digital currency. A central bank digital currency (CBDC) could well incorporate options and features specifically designed to execute cross-border payments, with a view to reducing the inefficiencies and rents discussed above by shortening the payments value chain. This report discusses the use of CBDCs for cross-border payments. The report reviews the models that have been developed for this purpose to date and discusses critical legal issues that arise in the context of cross-border use of CBDC. This report is organized as follows. Section II specifically discusses the models developed jointly by the Bank of Canada, Bank of England, and Monetary Authority of Singapore; Section III evaluates how cross-border CBDCs address challenges of the existing correspondent banking arrangement; Section IV discusses the legal issues involved in cross-border use of CBDCs, and Section V concludes the report with some general remarks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.008 |
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".