Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.005 |
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 it