Risks of the Use of FinTech in the Financial Inclusion of the Population: A Systematic Review of the Literature
Bibliographic record
Abstract
Financial technology (FinTech) has significantly changed access to financial services, particularly benefiting historically marginalized communities. While it offers many advantages, FinTech also brings substantial risks associated with this digital transformation. Recent studies highlight the significant impact of FinTech on financial inclusion, especially for marginalized populations. To investigate the benefits and drawbacks of FinTech and identify specific risks affecting users, particularly vulnerable groups, we employed the PRISMA method. A systematic literature review was conducted using the Web of Science database to explore recent research on FinTech and its relationship with financial inclusion, focusing on associated risks. The search covered 2010–2025; however, after applying inclusion criteria, the final dataset comprised publications from 2012 to 2025. Unlike previous bibliometric studies broadly addressing FinTech innovations, this review identifies and categorizes key risks affecting financial inclusion, emphasizing regulatory barriers, digital literacy, and socio-cultural challenges. The review is limited by the exclusive use of Web of Science and the English language, suggesting future research avenues using additional databases and multilingual sources. Findings reveal a notable increase in research activity surrounding FinTech and financial inclusion. This highlights challenges such as data privacy, regulation, and financial literacy. By mapping FinTech-related risks, this study aims to inform policymakers and stakeholders about effective strategies to mitigate these challenges and promote safe, inclusive financial ecosystems.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".