RECENT DEVELOPMENTS IN THE FINTECH INDUSTRY
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
In this article, we review some recent developments in the field of Financial Technology or “FinTech.” We begin with an overview of what FinTech is and why it has become an important growth industry in the financial services area and therefore an important research topic in finance. In the next section, we review some of the academic literature in the FinTech area. In the subsequent section, we characterize the financing of FinTech startups, especially by venture capital firms. In the following section, we characterize innovation by FinTech firms as well as by incumbent financial intermediaries. In the next section, we move on to discuss potential sources of value creation by FinTech start-up firms relative to existing incumbent firms: we conjecture that one source of value creation may arise from FinTech startups being able to provide a superior customer experience relative to incumbent firms in various areas of consumer finance. In the following section, we discuss the regulatory environment facing FinTech firms, in their banking as well as in their financial market activities. In the penultimate section, we analyze the buy-versus-build decision facing firms choosing to enter the FinTech sector and discuss the trade-offs that may drive such decisions in practice. We conclude with some remarks about the future directions that may be taken by the FinTech 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it