Fintechs and Institutions: Evidence from an Emerging Economy
Bibliographic record
Abstract
Institutions play a vital role in restricting or encouraging the performance of any economic agent. In this context, fintechs represent a vector of exponential change in the global financial system and its institutions. However, despite the existing relationship between fintechs and institutions, there is a need for more studies exploring the connections between them. Beginning with a framework that integrates aspects of the relation between fintechs and institutions in the financial system, the objective of this article is to empirically demonstrate the interaction between fintechs and financial system institutions in an emerging country. To do so, the chosen research method was an embedded case study, which involved documental analysis and semi-structured interviews conducted with different agents in the Brazilian financial system, belonging to the following categories: technology providers, fintechs, regulatory institutions, financial institutions, and consumers. The findings validate the applicability of the theoretical framework, highlighting that fintechs drive institutional changes across stakeholders with different characteristic traits. Based on these results, we created theoretical propositions that guide future studies on the topic of fintechs and institutions. This study’s contributions provide valuable insights for financial policymakers, regulators, and technology providers, particularly regarding the adaptation of regulatory frameworks and technological infrastructures in emerging economies. For policymakers, this study suggests guidelines to foster financial inclusion through fintech initiatives, while managers are encouraged to develop strategies that reduce operational gaps in digital financial services.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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".