The Relative Benefits and Risks of Stablecoins as a Means of Payment: A Case Study Perspective
Bibliographic record
Abstract
Our paper contributes to the discussion about the utility of stablecoins for retail payments through an objective, evidence-based approach that compares stablecoins with traditional retail payment methods. The paper also provides insights that could be useful in the design of central bank digital currencies. We identify the potential benefits, risks and costs of stablecoin arrangements used for retail payments relative to traditional retail payment methods. We select three real-world examples for comparison: (i) a Mastercard credit card payment through a traditional bank; (ii) a Unified Payments Interface fast payment through Paytm (a technology-enabled payments company regulated as a limited-purpose bank); and (iii) a stablecoin retail transaction using USD Coin and a BitPay wallet. We find that certain stablecoin arrangements offer end users greater control of their privacy, facilitate more rapid innovation and have the potential to increase transaction speeds, particularly for cross-border payments. At the same time, stablecoins may provide less consumer protection for fraud, present higher risks to the payment system and to efforts to combat financial crime (partly because of the more nascent regulatory framework), and be costlier relative to traditional payment arrangements. Our findings suggest that stablecoin arrangements do not currently serve as substitutes for the suite of traditional payment arrangements but instead address niche use cases or user segments that value their benefits and can accept their risks or costs.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".