Innovative disruption in financial technology and payment systems
Bibliographic record
Abstract
Purpose: This study explores the transformative impact of financial technology (fintech) on the global financial services industry, focusing on innovations, regulatory implications, and challenges. The research aims to identify key technological disruptions, examine the regulatory landscape, and highlight opportunities and risks introduced by fintech. Methodology/approach: A Systematic Literature Review (SLR) was conducted using SCOPUS, IEEE Xplore, and ScienceDirect. Following a structured protocol, 153 peer-reviewed articles (2014–2019) were analysed through thematic and meta-analytical approaches. The study adopted an interpretative philosophy and used the PICOC framework to refine search precision and synthesis. Results/findings: The analysis reveals fintech’s disruptive innovations in financing and payment systems, such as peer-to-peer (P2P) lending, crowdfunding, blockchain-enabled transactions, and mobile payments. These services have enhanced financial inclusion, operational efficiency, and customer accessibility. Regulatory frameworks have evolved in parallel, though challenges remain in addressing moral hazard, cybersecurity, and compliance. Geographically, Asia, particularly China and Indonesia, leads fintech research and implementation. Conclusion: Fintech has significantly reshaped financial ecosystems by enabling decentralized financial services, accelerating digital transactions, and fostering inclusivity. However, cybersecurity risks, limited regulatory clarity, and uneven global adoption continue to impede its sustainable integration. Limitations: The study is limited to English-language literature from 2014–2019 and may not capture recent post-pandemic developments or region-specific innovations in Islamic or informal economies. Contribution: This paper contributes a comprehensive synthesis of fintech’s evolution, identifies existing gaps, and offers insights for policymakers, financial institutions, and researchers to foster a balanced, secure, and innovative financial environment.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".