Strengthening AML/CFT and Proliferation Financing Frameworks for Stablecoins
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Stablecoins are rapidly transforming digital finance by enabling fast, low-cost transactions, cross-border payments, and greater financial inclusion in remittance-dependent Commonwealth economies. However, their pseudonymous nature, integration with decentralised finance, and global accessibility introduce significant vulnerabilities to money laundering, terrorist financing, and proliferation financing. Illicit actors increasingly exploit stablecoins to bypass traditional financial controls, sanctions, and oversight. This chapter evaluates these emerging risks and advances a coherent regulatory response anchored in the Commonwealth Model Law. It recommends risk-based supervision, stronger licensing and enforcement for issuers and intermediaries, Travel Rule compliance, enhanced due diligence, and mandatory transparency in reserves and governance. It also calls for improved data sharing, cyber-resilience standards, and capacity building for smaller jurisdictions. Case studies from across the Commonwealth illustrate both challenges and successful approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it