Trend AnalysisEconomics & FinanceMixed Methods
Fintech and Financial Inclusion: Unequal Benefits in Developing Countries
Mobile money accounts now outnumber traditional bank accounts in several Sub-Saharan African nations. In Kenya, M-Pesa processes more transactions than the country's commercial banking system combi...
By Sean K.S. Shin
This blog summarizes research trends based on published paper abstracts. Specific numbers or findings may contain inaccuracies. For scholarly rigor, always consult the original papers cited in each post.
Mobile money accounts now outnumber traditional bank accounts in several Sub-Saharan African nations. In Kenya, M-Pesa processes more transactions than the country's commercial banking system combined. The narrative is compelling: fintech leapfrogs legacy infrastructure, bringing the unbanked into the financial system. But the research tells a more complicated story. The benefits of fintech-driven financial inclusion are realโand they are distributed unevenly.
The Research Landscape
The Aggregate Promise
Azmeh and Al-Raeei (2024), publishing in PLOS ONE provide the most rigorous cross-country evidence. Using panel regression with Panel-Corrected Standard Errors across developing nations, they find that fintech and financial inclusion operate as both complements and substitutes depending on institutional context. A 1% increase in fintech adoption is associated with measurable improvements in the financial inclusion indexโbut the effect is not uniform. Countries with stronger regulatory frameworks and higher baseline digital literacy capture disproportionately larger gains.
Mashoene, Tweneboah, and Schaling (2025), publishing in Cogent Social Sciences construct a new financial inclusion index using Principal Component Analysis across 28 emerging and developing economies. Their System GMM model confirms that a 1% increase in fintech leads to a 0.1772 unit rise in the financial inclusion index. The finding is statistically robustโbut the authors also identify that education levels, GNI per capita, and broad money to GDP ratio significantly predict barriers to inclusion.
Where Benefits Concentrate
Mpofu (2024), published in the International Journal of Economics and Financial Issues offers the most comprehensive assessment of Industry 4.0's impact on digital financial services in developing countries. The analysis reveals a consistent pattern: fintech adoption benefits cluster among the "near-bankable"โpopulations who were already close to formal financial access but lacked convenient channels. The truly marginalizedโrural populations without smartphones, women in patriarchal economies with restricted financial autonomy, elderly populations with limited digital literacyโoften remain excluded even as aggregate inclusion numbers improve.
The Digital Divide Within Inclusion
Setyanti, Khoiruddin, and Finuliyah (2025) examine Indonesian household-level microdata and find that only 5.48% of households report using fintech services. The adoption determinants are telling: education, urban residence, and existing digital infrastructure access are the strongest predictors. This suggests that fintech may accelerate existing patterns of inclusion rather than disrupt them.
Khan, Zeb, and Kamal (2025) investigate the mediating role of socioeconomic status in fintech adoption. Their findings indicate that socioeconomic disparities actively shape individuals' capacity to engage with digital financial solutionsโfintech does not neutralize pre-existing inequality but interacts with it.
Critical Analysis: Claims and Evidence
<
| Claim | Evidence | Assessment |
|---|
| Fintech increases financial inclusion in developing economies | Azmeh & Al-Raeei (2024): panel regression across developing nations; Mashoene et al. (2025): GMM model, 28 EMDEs | Supported โ consistent cross-country evidence |
| Benefits accrue disproportionately to the near-bankable | Mpofu (2024): comprehensive review; Setyanti et al. (2025): 5.48% household adoption in Indonesia | Supported โ multiple data sources converge |
| Digital literacy is a binding constraint | Mashoene et al. (2025): education as key barrier; Khan et al. (2025): SES as mediator | Supported โ identified across methodologies |
| Fintech alone reduces poverty | Azmeh (2025): positive association but institutional dependence | Partially supported โ conditional on institutional quality |
| Gender gap narrows with fintech | Kumar et al. (2024): women's fintech adoption barriers in rural India | Mixed โ barriers persist, some progress in urban areas |
Open Questions and Future Directions
The last-mile problem: If fintech benefits concentrate among the near-bankable, what interventions reach the truly excluded? Agent banking networks, community-based financial literacy programs, and offline-compatible solutions need rigorous evaluation.Regulatory asymmetry: Strong regulation correlates with better fintech outcomes, but most developing countries face regulatory capacity constraints. How can lightweight regulatory frameworks capture fintech benefits without the institutional infrastructure of advanced economies?Gender-specific barriers: Women's exclusion from fintech benefits is not simply a "digital literacy" problemโit involves cultural norms around financial autonomy, phone ownership patterns, and household decision-making structures. Gender-targeted research designs are needed.Data exploitation risk: As fintech expands in developing countries, so does the collection of granular financial data on vulnerable populations. The relationship between financial inclusion and data protection is underexplored.Long-run effects: Most evidence is cross-sectional or covers short panels. Whether fintech-driven inclusion translates into sustained economic mobility remains an open empirical question.What This Means for Your Research
The fintech-inclusion narrative is not wrongโit is incomplete. Researchers should resist treating "financial inclusion" as a binary variable and instead examine who gains access, to what services, under what conditions, and with what outcomes. The field needs more disaggregated analysis: by gender, by rural-urban divide, by income quintile, and by type of financial service (payments vs. credit vs. savings vs. insurance).
Explore related work through ORAA ResearchBrain.
๋ฉด์ฑ
์กฐํญ: ์ด ๊ฒ์๋ฌผ์ ์ ๋ณด ์ ๊ณต์ ๋ชฉ์ ์ผ๋ก ํ ์ฐ๊ตฌ ๋ํฅ ๊ฐ์์ด๋ค. ํ์ ๋
ผ๋ฌธ์ ์ธ์ฉํ๊ธฐ ์ ์ ๊ตฌ์ฒด์ ์ธ ์ฐ๊ตฌ ๊ฒฐ๊ณผ, ํต๊ณ ๋ฐ ์ฃผ์ฅ์ ์๋ฌธ ๋
ผ๋ฌธ์ ํตํด ๋ฐ๋์ ํ์ธํด์ผ ํ๋ค.
ํํ
ํฌ์ ๊ธ์ต ํฌ์ฉ: ๊ฐ๋ฐ๋์๊ตญ์์์ ๋ถ๊ท ๋ฑํ ํํ
๋ชจ๋ฐ์ผ ๋จธ๋ ๊ณ์ข ์๊ฐ ์ฌํ๋ผ ์ด๋จ ์ํ๋ฆฌ์นด ์ฌ๋ฌ ๊ตญ๊ฐ์์ ์ ํต์ ์ธ ์ํ ๊ณ์ข ์๋ฅผ ์ด๋ฏธ ๋์ด์ฐ๋ค. ์ผ๋์์๋ M-Pesa๊ฐ ์ฒ๋ฆฌํ๋ ๊ฑฐ๋๋์ด ๊ตญ๋ด ์์
์ํ ์์คํ
์ ์ฒด๋ฅผ ํฉ์น ๊ฒ๋ณด๋ค ๋ง๋ค. ํํ
ํฌ๊ฐ ๊ธฐ์กด ์ธํ๋ผ๋ฅผ ๋ฐ์ด๋์ด ๋น์ํ ์ธ๊ตฌ๋ฅผ ๊ธ์ต ์์คํ
์ผ๋ก ํธ์
์ํจ๋ค๋ ์์ฌ๋ ์ค๋๋ ฅ์ด ์๋ค. ๊ทธ๋ฌ๋ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ ๋ณด๋ค ๋ณต์กํ ์ด์ผ๊ธฐ๋ฅผ ๋ค๋ ค์ค๋ค. ํํ
ํฌ ์ฃผ๋ ๊ธ์ต ํฌ์ฉ์ ํํ์ ์ค์ฌํ์ง๋ง, ๊ทธ ๋ถ๋ฐฐ๋ ๋ถ๊ท ๋ฑํ๋ค.
์ฐ๊ตฌ ๋ํฅ
์ด์ฒด์ ๊ฐ๋ฅ์ฑ
PLOS ONE์ ๊ฒ์ฌ๋ Azmeh and Al-Raeei(2024)๋ ๊ฐ์ฅ ์๋ฐํ ๊ตญ๊ฐ ๊ฐ ๋น๊ต ๊ทผ๊ฑฐ๋ฅผ ์ ์ํ๋ค. ๊ฐ๋ฐ๋์๊ตญ์ ๋์์ผ๋ก ํจ๋ ์์ ํ์ค ์ค์ฐจ(Panel-Corrected Standard Errors)๋ฅผ ์ ์ฉํ ํจ๋ ํ๊ท ๋ถ์์ ํตํด, ์ด๋ค์ ํํ
ํฌ์ ๊ธ์ต ํฌ์ฉ์ด ์ ๋์ ๋งฅ๋ฝ์ ๋ฐ๋ผ ๋ณด์์ฌ์ ๋์ฒด์ฌ๋ก ๋ชจ๋ ๊ธฐ๋ฅํ๋ค๋ ์ฌ์ค์ ๋ฐํ๋ค. ํํ
ํฌ ์ฑํ๋ฅ ์ด 1% ์ฆ๊ฐํ๋ฉด ๊ธ์ต ํฌ์ฉ ์ง์๊ฐ ์ธก์ ๊ฐ๋ฅํ ์์ค์ผ๋ก ๊ฐ์ ๋์ง๋ง, ๊ทธ ํจ๊ณผ๋ ๊ท ์ผํ์ง ์๋ค. ๊ท์ ์ฒด๊ณ๊ฐ ๊ฐ๋ ฅํ๊ณ ๊ธฐ์ด ๋์งํธ ๋ฆฌํฐ๋ฌ์ ์์ค์ด ๋์ ๊ตญ๊ฐ์ผ์๋ก ๋ถ๊ท ํ์ ์ผ๋ก ๋ ํฐ ํจ๊ณผ๋ฅผ ์ป๋๋ค.
Cogent Social Sciences์ ๊ฒ์ฌ๋ Mashoene, Tweneboah, and Schaling(2025)์ 28๊ฐ ์ ํฅยท๊ฐ๋ฐ๋์๊ตญ์ ๋์์ผ๋ก ์ฃผ์ฑ๋ถ ๋ถ์(Principal Component Analysis)์ ํ์ฉํ์ฌ ์๋ก์ด ๊ธ์ต ํฌ์ฉ ์ง์๋ฅผ ๊ตฌ์ถํ๋ค. ์ด๋ค์ ์์คํ
GMM ๋ชจํ์ ํํ
ํฌ๊ฐ 1% ์ฆ๊ฐํ ๋ ๊ธ์ต ํฌ์ฉ ์ง์๊ฐ 0.1772 ๋จ์ ์์นํจ์ ํ์ธํ๋ค. ์ด ๊ฒฐ๊ณผ๋ ํต๊ณ์ ์ผ๋ก ๊ฐ๊ฑดํ์ง๋ง, ์ ์๋ค์ ๊ต์ก ์์ค, 1์ธ๋น GNI, GDP ๋๋น ๊ด์ ํตํ ๋น์จ์ด ๊ธ์ต ํฌ์ฉ์ ์ฅ๋ฒฝ์ ์ ์๋ฏธํ๊ฒ ์์ธกํจ์ ๋์์ ๊ท๋ช
ํ๋ค.
ํํ์ด ์ง์ค๋๋ ๊ณณ
International Journal of Economics and Financial Issues์ ๊ฒ์ฌ๋ Mpofu(2024)๋ ๊ฐ๋ฐ๋์๊ตญ์ ๋์งํธ ๊ธ์ต ์๋น์ค์ ๋ํ Industry 4.0์ ์ํฅ์ ๊ฐ์ฅ ํฌ๊ด์ ์ผ๋ก ํ๊ฐํ๋ค. ์ด ๋ถ์์ ์ผ๊ด๋ ํจํด์ ๋๋ฌ๋ธ๋ค. ํํ
ํฌ ์ฑํ์ ํํ์ ์ด๋ฏธ ๊ณต์ ๊ธ์ต ์๋น์ค ์ ๊ทผ์ ๊ทผ์ ํด ์์์ง๋ง ํธ๋ฆฌํ ์ฑ๋์ด ์์๋ ์ธ๊ตฌ์ธ '์ค(ๆบ)์ํ ์ด์ฉ ๊ฐ๋ฅ ๊ณ์ธต(near-bankable)'์ ์ง์ค๋๋ค. ์ค๋งํธํฐ์ด ์๋ ๋์ด ์ธ๊ตฌ, ๊ธ์ต ์์จ์ฑ์ด ์ ํ๋ ๊ฐ๋ถ์ฅ์ ๊ฒฝ์ ์ฒด์ ์ ์ฌ์ฑ, ๋์งํธ ๋ฆฌํฐ๋ฌ์๊ฐ ๋ถ์กฑํ ๊ณ ๋ น ์ธ๊ตฌ ๋ฑ ์ง์ ์ผ๋ก ์์ธ๋ ๊ณ์ธต์ ์ด์ฒด์ ๊ธ์ต ํฌ์ฉ ์์น๊ฐ ๊ฐ์ ๋๋ ์ค์๋ ์ฌ์ ํ ๋ฐฐ์ ๋ ์ํ๋ก ๋จ๋ ๊ฒฝ์ฐ๊ฐ ๋ง๋ค.
๊ธ์ต ํฌ์ฉ ๋ด์ ๋์งํธ ๊ฒฉ์ฐจ
Setyanti, Khoiruddin, and Finuliyah(2025)๋ ์ธ๋๋ค์์ ๊ฐ๊ตฌ ์์ค์ ๋ง์ดํฌ๋ก๋ฐ์ดํฐ๋ฅผ ๋ถ์ํ์ฌ ํํ
ํฌ ์๋น์ค๋ฅผ ์ด์ฉํ๋ค๊ณ ์๋ตํ ๊ฐ๊ตฌ๊ฐ ์ ์ฒด์ 5.48%์ ๋ถ๊ณผํจ์ ๋ฐ๊ฒฌํ๋ค. ์ฑํ์ ๊ฒฐ์ ์์ธ์ ์์ฌํ๋ ๋ฐ๊ฐ ํฌ๋ค. ๊ต์ก ์์ค, ๋์ ๊ฑฐ์ฃผ ์ฌ๋ถ, ๊ธฐ์กด ๋์งํธ ์ธํ๋ผ ์ ๊ทผ์ฑ์ด ๊ฐ์ฅ ๊ฐ๋ ฅํ ์์ธก ๋ณ์์ด๋ค. ์ด๋ ํํ
ํฌ๊ฐ ๊ธฐ์กด์ ๊ธ์ต ํฌ์ฉ ํจํด์ ๋ณํ์ํค๊ธฐ๋ณด๋ค๋ ์คํ๋ ค ๊ฐ์ํํ ์ ์์์ ์์ฌํ๋ค.
Khan, Zeb, and Kamal(2025)์ ํํ
ํฌ ์ฑํ์์ ์ฌํ๊ฒฝ์ ์ ์ง์์ ๋งค๊ฐ ์ญํ ์ ํ๊ตฌํ๋ค. ์ด๋ค์ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ ์ฌํ๊ฒฝ์ ์ ๊ฒฉ์ฐจ๊ฐ ๋์งํธ ๊ธ์ต ์๋ฃจ์
์ฐธ์ฌ ์ญ๋์ ๋ฅ๋์ ์ผ๋ก ํ์ฑํ๋ค๋ ์ ์ ๋ณด์ฌ์ค๋ค. ์ฆ, ํํ
ํฌ๋ ๊ธฐ์กด์ ๋ถํ๋ฑ์ ์ค๋ฆฝํํ๋ ๊ฒ์ด ์๋๋ผ ๊ทธ๊ฒ๊ณผ ์ํธ์์ฉํ๋ค.
๋นํ์ ๋ถ์: ์ฃผ์ฅ๊ณผ ๊ทผ๊ฑฐ
<
| ์ฃผ์ฅ | ๊ทผ๊ฑฐ | ํ๊ฐ |
|---|
| ํํ
ํฌ๋ ๊ฐ๋ฐ๋์๊ตญ์ ๊ธ์ต ํฌ์ฉ์ ์ฆ๋์ํจ๋ค | Azmeh & Al-Raeei(2024): ๊ฐ๋ฐ๋์๊ตญ ๋์ ํจ๋ ํ๊ท ๋ถ์; Mashoene et al.(2025): GMM ๋ชจํ, 28๊ฐ ์ ํฅยท๊ฐ๋ฐ๋์๊ตญ(EMDEs) | ์ง์ง๋จ โ ๊ตญ๊ฐ ๊ฐ ๋น๊ต์์ ์ผ๊ด๋ ๊ทผ๊ฑฐ ํ์ธ |
| ํํ์ ์ค์ํ ์ด์ฉ ๊ฐ๋ฅ ๊ณ์ธต์ ๋ถ๊ท ํ์ ์ผ๋ก ์ง์ค๋๋ค | Mpofu(2024): ํฌ๊ด์ ๊ฒํ ; Setyanti et al.(2025): ์ธ๋๋ค์์ ๊ฐ๊ตฌ ์ฑํ๋ฅ 5.48% | ์ง์ง๋จ โ ๋ณต์์ ๋ฐ์ดํฐ ์ถ์ฒ๊ฐ ์๋ ด |
| ๋์งํธ ๋ฆฌํฐ๋ฌ์๋ ๊ตฌ์์ ์ ์ฝ์ด๋ค | Mashoene et al. (2025): ์ฃผ์ ์ฅ๋ฒฝ์ผ๋ก์์ ๊ต์ก; Khan et al. (2025): ๋งค๊ฐ๋ณ์๋ก์์ SES | ์ง์ง๋จ โ ์ฌ๋ฌ ๋ฐฉ๋ฒ๋ก ์ ๊ฑธ์ณ ํ์ธ๋จ |
| Fintech๋ง์ผ๋ก ๋น๊ณค์ ๊ฐ์์ํจ๋ค | Azmeh (2025): ๊ธ์ ์ ์ฐ๊ด์ฑ์ด ์์ผ๋ ์ ๋์ ์์กด์ฑ ์กด์ฌ | ๋ถ๋ถ์ ์ผ๋ก ์ง์ง๋จ โ ์ ๋์ ์ง์ ์กฐ๊ฑด๋ถ |
| Fintech๋ก ์ฑ๋ณ ๊ฒฉ์ฐจ๊ฐ ์ขํ์ง๋ค | Kumar et al. (2024): ์ธ๋ ๋์ด ์ง์ญ ์ฌ์ฑ์ fintech ๋์
์ฅ๋ฒฝ | ํผ์ฌ๋จ โ ์ฅ๋ฒฝ์ด ์ง์๋๋, ๋์ ์ง์ญ์์๋ ์ผ๋ถ ์ง์ |
๋ฏธํด๊ฒฐ ์ง๋ฌธ๊ณผ ํฅํ ๋ฐฉํฅ
๋ผ์คํธ๋ง์ผ ๋ฌธ์ : Fintech์ ํํ์ด ๊ธ์ต ์๋น์ค ์ด์ฉ ๊ฐ๋ฅ์ฑ์ด ๋์ ๊ณ์ธต์ ์ง์ค๋๋ค๋ฉด, ์ง์ ์ผ๋ก ์์ธ๋ ๊ณ์ธต์๊ฒ ๋๋ฌํ๊ธฐ ์ํ ๊ฐ์
์ ๋ฌด์์ธ๊ฐ? ์์ด์ ํธ ๋ฑ
ํน ๋คํธ์ํฌ, ์ง์ญ์ฌํ ๊ธฐ๋ฐ ๊ธ์ต ๋ฆฌํฐ๋ฌ์ ํ๋ก๊ทธ๋จ, ์คํ๋ผ์ธ ํธํ ์๋ฃจ์
์ ๋ํ ์๋ฐํ ํ๊ฐ๊ฐ ํ์ํ๋ค.๊ท์ ๋น๋์นญ: ๊ฐ๋ ฅํ ๊ท์ ๋ ๋ ๋์ fintech ์ฑ๊ณผ์ ์๊ด๊ด๊ณ๋ฅผ ๋ณด์ด์ง๋ง, ๋๋ถ๋ถ์ ๊ฐ๋ฐ๋์๊ตญ์ ๊ท์ ์ญ๋์ ์ ์ฝ์ ์ง๋ฉดํด ์๋ค. ๊ฒฝ๋ํ๋ ๊ท์ ํ๋ ์์ํฌ๋ ์ ์ง๊ตญ์ ์ ๋์ ์ธํ๋ผ ์์ด๋ fintech์ ํํ์ ์ด๋ป๊ฒ ํฌ์ฐฉํ ์ ์๋๊ฐ?์ฑ๋ณ ํน์ ์ ์ฅ๋ฒฝ: Fintech ํํ์์ ์ฌ์ฑ์ด ๋ฐฐ์ ๋๋ ๊ฒ์ ๋จ์ํ "๋์งํธ ๋ฆฌํฐ๋ฌ์" ๋ฌธ์ ๊ฐ ์๋๋ฉฐ, ๊ธ์ต ์์จ์ฑ์ ๊ดํ ๋ฌธํ์ ๊ท๋ฒ, ํด๋ํฐ ์์ ํจํด, ๊ฐ๊ตฌ ๋ด ์์ฌ๊ฒฐ์ ๊ตฌ์กฐ์ ๊ด๋ จ๋๋ค. ์ฑ๋ณ์ ๋์์ผ๋ก ํ ์ฐ๊ตฌ ์ค๊ณ๊ฐ ํ์ํ๋ค.๋ฐ์ดํฐ ์ฐฉ์ทจ ์ํ: Fintech๊ฐ ๊ฐ๋ฐ๋์๊ตญ์์ ํ์ฅ๋จ์ ๋ฐ๋ผ ์ทจ์ฝ ๊ณ์ธต์ ๋ํ ์ธ๋ถํ๋ ๊ธ์ต ๋ฐ์ดํฐ ์์ง๋ ์ฆ๊ฐํ๋ค. ๊ธ์ต ํฌ์ฉ๊ณผ ๋ฐ์ดํฐ ๋ณดํธ ๊ฐ์ ๊ด๊ณ๋ ์ถฉ๋ถํ ํ๊ตฌ๋์ง ์์ ์ํ์ด๋ค.์ฅ๊ธฐ์ ํจ๊ณผ: ๋๋ถ๋ถ์ ์ฆ๊ฑฐ๋ ํก๋จ๋ฉด์ ์ด๊ฑฐ๋ ์งง์ ํจ๋์ ๋ค๋ฃจ๊ณ ์๋ค. Fintech ๊ธฐ๋ฐ์ ํฌ์ฉ์ด ์ง์์ ์ธ ๊ฒฝ์ ์ ์ด๋์ฑ์ผ๋ก ์ด์ด์ง๋์ง๋ ์ฌ์ ํ ๋ฏธํด๊ฒฐ๋ ์ค์ฆ์ ์ง๋ฌธ์ผ๋ก ๋จ์ ์๋ค.์ฐ๊ตฌ์ ๋ํ ์์ฌ์
Fintech-ํฌ์ฉ ์์ฌ๊ฐ ํ๋ฆฐ ๊ฒ์ด ์๋๋ผ ๋ถ์์ ํ ๊ฒ์ด๋ค. ์ฐ๊ตฌ์๋ค์ "๊ธ์ต ํฌ์ฉ"์ ์ด๋ถ๋ฒ์ ๋ณ์๋ก ์ทจ๊ธํ๋ ๊ฒ์ ์ง์ํ๊ณ , ๋๊ฐ ์ด๋ค ์๋น์ค์, ์ด๋ค ์กฐ๊ฑด ํ์์, ์ด๋ค ์ฑ๊ณผ๋ฅผ ์ป์ผ๋ฉฐ ์ ๊ทผํ๋์ง๋ฅผ ๊ฒํ ํด์ผ ํ๋ค. ์ด ๋ถ์ผ๋ ๋ ์ธ๋ถํ๋ ๋ถ์์ด ํ์ํ๋ค: ์ฑ๋ณ, ๋์ด-๋์ ๊ตฌ๋ถ, ์๋ ๋ถ์, ๊ทธ๋ฆฌ๊ณ ๊ธ์ต ์๋น์ค ์ ํ(๊ฒฐ์ ๋ ์ ์ฉ ๋ ์ ์ถ ๋ ๋ณดํ)์ ๋ฐ๋ฅธ ๋ถ์์ด ์๊ตฌ๋๋ค.
๊ด๋ จ ์ฐ๊ตฌ๋ ORAA ResearchBrain์ ํตํด ํ์ํ ์ ์๋ค.
References (5)
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