Digital Finance Adoption in Brazil: An Exploratory Analysis on Financial Apps and Digital Financial Literacy
Bibliographic record
Abstract
Digital transformation has fundamentally altered how individuals manage their finances. The expansion of financial technologies and the digitalization of banking services underscore the need for digital financial literacy, defined as the ability to safely use financial applications and make informed decisions within virtual environments. This study examined the perceptions of financial application use across age groups and their corresponding level of digital financial literacy. This exploratory study used a convenience sample of 41 semi-structured interviews conducted in 2025. The data were analyzed using content analysis and descriptive statistics. The findings indicated that most participants prioritized digital apps over traditional channels and expressed confidence in their use, although concerns about data security remained. Participants identified key advantages, including convenience, efficiency, and centralized access, yet few used apps for financial planning. Most respondents demonstrated an intermediate level of digital knowledge, with limited proficiency in executing complex financial tasks. Perceptions revealed both optimism and apprehension: while participants valued the practicality of digital tools, they also recognized risks such as fraud, exclusion of vulnerable groups, and technological dependence. The limited and non-representative sample limits generalization, suggesting the need for broader surveys. Enhanced public policies promoting digital financial education in Brazil are recommended.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".