Bibliographic record
Abstract
This comprehensive analysis delves into the driving forces behind the rise of financial technology, its disruptive effects on conventional banking systems, and the increasingly pivotal role of artificial intelligence within the industry. Over the years, FinTech has revolutionized the financial landscape by introducing cutting-edge solutions in areas like payment processing, credit evaluation, and client engagement. The discussion underscores how the evolution of traditional banking practices, regulatory frameworks, and consumer confidence have collectively influenced the global expansion of FinTech. Furthermore, it explores how AI is reshaping the sector by streamlining financial operations, refining risk assessment strategies, and elevating user experiences. Despite these advances, a significant research gap remains regarding the broader implications of AI on FinTech, particularly in terms of transparency, fairness, and accountability. This gap suggests the need for further research to address critical issues such as data bias, decision-making transparency, and the regulatory challenges posed by AI-driven financial services. The review underscores the importance of developing robust frameworks that allow for the responsible integration of AI in FinTech, ensuring innovation while maintaining ethical standards and consumer trust.
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.
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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".