Fintech and the Digital Transformation of Financial Services
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
Fintech is one of the most important research subjects nowadays. China’s financial computerizing has gradually realized the traditional business, which built a customer-centric electronic financial service system, and the rapid development of mobile banking, peer-to-peer payment services, and trading platforms. Under the backdrop of the rapid development of financial computerization, there are a series of clear and potential risks in financial computerization. Therefore, the research topic of this paper is the impact of today’s digitalization on the development of financialization. This paper will collect the data of today’s digital financialization application and analyze these data from the perspective of finance. At present, the financial industry and technological innovation are increasingly closely linked, and technology-driven financial innovation covers the whole world. In addition, the COVID-19 pandemic is accelerating the global digitization process, accelerating the competitive development of global big data and digital economy. As isolation measures increase the demand for telecommuting and online education, the global demand for broadband communications services has soared. Meanwhile, the consumption of content based on short videos and live broadcasts has soared, resulting in a rapid increase in the amount of information created and captured worldwide. As a result, the progress of fintech has promoted the great prosperity of the financial industry.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it