"Regulators Nurturing Fintech Innovation: Global Evolution of the Regulatory Sandbox as Opportunity-Based Regulation"
Bibliographic record
Abstract
"The regulatory sandbox is a real-world alternative to regulatory lag. Its emergence as a novel regulatory development responds to challenges faced by FinTech innovators in navigating an unwieldy regulatory landscape not designed with FinTech in mind. Regulatory sandboxes are in operation in developed countries including Australia, Canada, Denmark, Hong Kong, Singapore, Switzerland, the Netherlands, the United Arab Emirates, the United Kingdom, and the United States. Within the European Union they are seen in Denmark, Hungary, Lithuania, Poland and the Netherlands. The concept has also gained traction with regulators in developing countries such as India, Indonesia, Malaysia, Mauritius and Thailand. Not only is the regulatory sandbox an experimental space for firms testing innovative FinTech products and services, it is also a novel regulatory experiment for regulators. This article advances the available literature through focusing on the contradictions inherent in the role of the regulator in administering a regulatory sandbox. It characterises the regulatory sandbox as a form of agile, opportunity-based regulation, distinguished by a regulatory approach that is concerned with actively supporting innovators in nurturing cutting-edge innovation to benefit innovators, consumers, investors, and the wider economy. This is path-breaking regulatory territory. In its provision and design, a regulatory sandbox performs a crucial signalling function in relation to a given financial system’s receptivity to FinTech business. An economic, pro-innovation agenda is at work. Distinct policy questions are therefore raised regarding the legitimate role of public gatekeeper financial services regulators operating regulatory sandboxes. The role of a regulatory sandbox in nurturing and expanding competition suggests a public interest role in the interests of consumer choice, price and efficiency rather than simply on risk minimisation. However, pressure on regulators to produce sandbox successes and to compete with other sandboxes may influence the exercise of regulatory discretion and produce regulatory distortions that affect competition in FinTech markets."
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".