Banking in the Age of Blockchain and FinTech: A Hybrid Efficiency Framework for Emerging Economies
Bibliographic record
Abstract
In the present era where digitalization, FinTech, and blockchain technologies are reshaping the global financial landscape, traditional measures of bank performance need to evolve. This paper introduces a hybrid approach that combines multi-criteria efficiency assessment and econometric modeling to evaluate bank performance within the context of digital transformation in emerging economies. Focusing on a panel of banks across selected emerging markets, this study first applies a multi-criteria decision-making technique (Data Envelopment Analysis) to assess operational efficiency using both conventional indicators and digitalization-driven metrics, such as mobile banking penetration and blockchain adoption. We then employ a panel econometric model to investigate the factors that shape efficiency outcomes, with special attention to FinTech and blockchain innovations as potential drivers. The results reveal a nuanced picture of how digital technologies can influence bank performance, highlighting both opportunities and constraints for financial institutions in less developed markets. The findings offer actionable insights for bank managers, regulators, and policymakers striving to balance traditional operational priorities with the demands of digital transformation. By linking efficiency measurement with an examination of the digitalization process, this paper provides a timely contribution to the literature on banking and financial innovation, serving as a foundation for future research and strategic decision-making in the FinTech and blockchain era.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".