Regulations and Fintech: A Comparative Study of the Developed and Developing Countries
Bibliographic record
Abstract
Financial technology (Fintech) has influenced business by helping create better services for consumers and businesses. Fintech, however, brings new challenges for regulators, who struggle to keep pace with the constant evolution of technology and the resulting disruption. The progress of technology and regulations in the Fintech industry has been uneven across developed and developing countries, resulting in numerous opportunities and challenges. Considerable progress has recently been made in the adoption of Fintech and the subsequent development and implementation of regulations in the US, the UK, and India. While the United States (US) and the United Kingdom (UK) are global leaders in Fintech innovation, India has shown fast-paced growth in adopting and utilizing Fintech services. This paper examines the growth and evolution of Fintech in the US, the UK, and India and also explores how the regulatory agencies across these countries have responded to the evolution of Fintech. This paper finds that economies should work towards improving digital infrastructure, financial inclusion, and financial literacy and enhance the collaboration among regulators, Fintech firms, and other stakeholders.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".