Can central bank digital currencies help advance financial inclusion?
Bibliographic record
Abstract
Central banks around the world are considering how retail central bank digital currencies (CBDCs) may help to advance financial inclusion. While CBDCs are not a magic bullet, they could be a further tool to promote universal access to payments and other financial services if this goal features prominently in the design from the get-go. In particular, central banks can consider design options to: (1) promote innovation in the two-tiered financial system (eg allowing for non-bank payment service providers); (2) offer a robust and low-cost public sector technological basis (with novel interfaces and offline payments); (3) facilitate enrolment (via simplified due diligence and electronic know-your-customer processes) and data portability; and (4) foster interoperability (both domestically and across borders). Together, these features can address a range of specific barriers to financial inclusion: geographic remoteness, institutional and regulatory factors, economic and market structure issues, characteristics of vulnerability, lack of financial literacy and low trust in existing financial institutions. This paper draws on interviews with nine central banks with advanced work on CBDCs and financial inclusion — the Central Bank of the Bahamas, Bank of Canada, People’s Bank of China, Eastern Caribbean Central Bank, Bank of Ghana, Central Bank of Malaysia, Bangko Sentral ng Pilipinas, National Bank of Ukraine and Central Bank of Uruguay. It gives concrete examples from the central banks’ work and discusses challenges, risks and regulatory and legal implications. It argues that while CBDCs hold promise for furthering financial inclusion, CBDC issuance may also require new laws and regulations to be enacted, or existing laws to be revised.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".