A Review of Blockchain in Fintech: Taxonomy, Challenges, and Future Directions
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
The primary purpose of this paper is to bridge the technology gap between Blockchain and Fintech applications. Blockchain technology is already being explored in a wide number of Fintech sectors. After creating a unique taxonomy for Fintech ecosystems, this paper outlines a number of implementation scenarios. For each of the industries in which blockchain is already in use and has established itself as a complementary technology to traditional systems, we give a taxonomy of use cases. In this procedure, we cover both public and private blockchains. Because it is still believed to be in its infancy, especially when it comes to financial use cases, blockchain has both positive and negative aspects. As a result, it is critical to be aware of all of the open research issues in this field. Our goal is to compile a list of open research challenges related to various aspects of the blockchain’s protocol and application layers. Finally, we will provide a clear understanding of the applications for which blockchain can be valuable, as well as the risks associated with its use in parallel.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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