Fintech Converges with Investment and Risk: A Bibliometric Review
Bibliographic record
Abstract
The rapid growth of fintech is revolutionizing the delivery, access, and management of financial services. It also presents new risks and opportunities for investment. Despite growing scholarly interest, current research often remains fragmented and continues to explore technological innovation, investment behavior, and risk management in isolation without fully addressing their interrelated dynamics. This lack of integration has hindered a comprehensive understanding of how fintech transforms financial ecosystems and subsequent decision-making processes. By conducting a systematic literature review, study addresses the research gap by examining key developments, trends, and patterns within this field through bibliometric analysis using VOSviewer 1.6.20 and Biblioshiny 2025. From the perspectives of authors, affiliations, papers, and journals, this investigation identifies publication trends, key contributors, and geographic distribution. The results also indicate that numerous areas warrant further investigation, such as sustainability, inclusion, risk management, technologies, and behavioral traits. The review provides a more comprehensive understanding of how fintech is affecting investment practices and risk considerations by consolidating findings and analyses. It underscores substantial prospects for future research and fosters a more comprehensive academic dialogue, thereby facilitating the advancement of more informed, responsive, and forward-thinking fintech strategies in financial management.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.087 | 0.118 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".