Does Fintech-Driven Inclusive Finance Induce Bank Profitability? Empirical Evidence from Developing Countries
Bibliographic record
Abstract
This study explores the effect of fintech-driven inclusive finance on the profitability of banks using an unbalanced panel dataset from 660 banks across 40 developing countries between 2011 and 2021. We start with a fixed-effect estimate and subsequently validate our main findings using two-stage least squares (2SLS-IV), two-step system generalized method of moments (GMM), and generalized least squares (GLS) methodologies. Our analysis centers on three key profitability metrics: ROA, ROE, and NIM. Our findings suggest that fintech-backed inclusive finance boosts ROA by 9.10%, ROE by 18.87%, and NIM by 7.98%, highlighting the growing importance of mobile, internet, and agent banking in these nations. We also note that large banks benefit more from inclusive finance than small ones. Additionally, conventional banks see a more marked improvement in profitability than Islamic and savings banks. The relationship between inclusive finance and bank profitability is stronger in countries with higher GDP growth and those actively advancing financial inclusion through fintech, compared to countries with slower GDP growth and less emphasis on financial inclusion. When examining the interaction effects, the COVID-19 pandemic has further emphasized the positive connection between fintech and bank profitability. This suggests that fintech-driven inclusive finance can play a role in enhancing bank profitability, even in challenging times like the COVID-19 period. The transition towards fintech, however, mandates substantial investments, enhanced financial literacy, and heightened customer security, presenting persistent challenges for governments, policymakers, regulators, and financial institutions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".