Bibliographic record
Abstract
This article analyzes the Fintech evolution. After describing the process of this phenomenon, some of the main definitions are provided both nationally and internationally. Finally, six main models of Fintech are analyzed. Through a systematic literature, 14 articles have been selected that deal with the phenomenon of Fintech. Six Fintech business models implemented by the ever growing number of Fintech startups have been identified, payment, wealth management, crowdfunding, loan, capital market and insurance services. Internationally, Fintech has already been defined by the International Monetary Fund (IMF), the World Bank Group (WBG), the Financial Stability Board (FSB), the Organization for Economic Cooperation and Development (OECD), the International Organization of Securities Commissions (IOSCO), the Bank for International Settlements (BIS). On a national level, on the other hand, Fintech has been analyzed by various countries, USA, United Kingdom, Singapore, China, Switzerland, China, Australia and the European Union. Fintech refers to a broad set of innovations - observable in the financial field in a broad sense - which are made possible by the use of new technologies both in the offer of services to end users and in the internal production processes of financial operators as well as in the design of market enterprises, without thereby compromising new possible configurations of intersectoral activities. Fintech appears to be representative of innovative methods - based on technology - of carrying out activities directly or indirectly connected to financial services rather than being a pre-defined industrial sector. Following the logic of the digital economy, Fintech contributes to designing an open and continuous network of modular services for businesses, individuals and banking, financial and insurance intermediaries, becoming a powerful acceleration force for the integration policies of the financial services markets in the EU.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".