Why cash alone won’t cut it: Practical strategies to increase resilience in European payment infrastructure
Bibliographic record
Abstract
At a time when, as the European Card Payment Association puts it, ‘economic relationships between nations can quickly sour’,1 the issue of resilience in payment systems has become both more important and more urgent in the strategic plans of governments, financial institutions and businesses alike. Some European governments have tended to look at cash as the key to resilience in retail payments, advising citizens to hoard (non-interest-bearing) cash at home — but is this the best strategy? This paper examines both natural and man-made disasters to see what lessons we can learn about the use of cash as a backup to electronic payment systems. The paper concludes that rather than rely on cash alone, the best way to increase the overall resilience of both national and regional payment systems in general, and pan-European payments infrastructure in particular, is through the use of multiple, independent electronic payment systems alongside some cash. The paper further concludes that the addition of ‘offline’ payment capabilities would substantially increase resilience, and that this should therefore be a focus of interest for strategists in the field. The paper suggests four areas where European strategic planners might focus: aiding domestic schemes to evolve adjacent functionality (specifically, digital identity); accelerating the take-up of account-to-account payments; advancing the use of stablecoins; and advocating for the development of offline central bank digital currency. This article is also included in the Business & Management Collection which can be accessed at https://hstalks.com/business/.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.033 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 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".