The impact of FinTech on banking: a systematic literature review and research agenda including challenges and trends
Open accessAbstract
Purpose: Rapid technological advancement has accelerated the integration of financial technology (FinTech) into traditional banking systems. Banks have adopted digital payments, artificial intelligence, blockchain solutions and open banking frameworks, thereby increasing competition and prompting regulatory adaptation. This study conducts a theory guided systematic literature review and bibliometric analysis of FinTech banking research (2019–2024) to map the intellectual structure, thematic evolution and research gaps. Design/methodology/approach: The review analyses 224 peer reviewed journal articles indexed in the Web of Science Core Collection. Using BibExcel and VOSviewer, the study employs co-citation analysis, keyword co-occurrence mapping, clustering techniques and temporal overlay analysis. The review protocol follows explicit search strings, inclusion criteria and screening procedures to enhance transparency and replicability. Findings: Six major thematic domains emerge: competition and risk-taking dynamics, financial inclusion and regulatory boundaries, institutional technology integration, performance and efficiency outcomes, innovation and regulatory economics and digital transformation and adoption behaviour. Temporal analysis reveals a progression from adoption focused inquiry toward governance, competition and systemic stability debates. Despite increasing empirical sophistication, the field remains fragmented across behavioural, institutional and macroprudential levels. Originality/value: This study embeds bibliometric mapping within a multi-level theoretical framework integrating diffusion, disruptive innovation and ecosystem perspectives. The research provides a critical synthesis of the evolving FinTech banking literature. The findings identify key research gaps, reveal emerging thematic patterns in FinTech banking research and outline directions for future research while offering implications for banking practitioners and regulators.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 73
- Authors
- Bolfek, M., & Lu, K.
- Affiliations
- Brunel University of London (GB)
- ORCID
- Bolfek, Martin
- Venue
- Fintech and Digital Accounting Review
- Publisher
- Emerald
- Published
- 2026-08-14
- Pages
- 1-35
- ISSN
- 2977-7631 (ISSN-L)
- OpenAlex
- W7202391762
- Cited by
- 0
- References
- 73
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Fintech and Digital Accounting Review (publishedVersion, cc-by)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › FinTech, Crowdfunding, Digital Finance
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Digital Transformation in Financial Services
- Social Sciences › Economics, Econometrics and Finance › Finance › Banking stability, regulation, efficiency
- Keywords
- fintech, banking sector, digital transformation, open banking, blockchain, artificial intelligence in banking, digital payments, bank competition, risk-taking, financial inclusion, banking regulation, disruptive innovation, technology adoption, bank governance, research gaps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review