Lessons from Remarkable FinTech Companies for the Financial Inclusion in Peru
Bibliographic record
Abstract
Financial inclusion, defined as the adequate access and usage of formal financial services to improve people’s lives, is a crucial area for the economic development of a country through its various angles. This paper analyzes the impact of selected FinTech companies on financial inclusion in their respective countries to obtain lessons of their business models and country environments that can help Peruvian financial inclusion. The selected FinTechs are M-PESA in Kenya, Nubank in Brazil, GCASH in the Philippines, and Easypaisa in Pakistan, which revolutionized the financial sector in their respective countries. However, a comparative study of their impact on financial inclusion in their respective country has not been conducted yet; therefore, the lessons obtained are helpful for the Peruvian situation due to their practical implications and because they raise possible areas for further and deeper research. The approach of this study considered a qualitative and quantitative method (to find a Pearson correlation between the percentage of the population of Country (A) that are users of FinTech (a) and the six selected demand-side indicators per country retrieved from the Global Findex Database) analysis to understand the results obtained. The results obtained indicate that M-PESA and GCASH, companies specialized in providing basic mobile money transactions such as remittances and withdrawals, did not impact the provision of other financial services such as savings or credit cards. In Easypaisa’s case, this company positively impacts the studied indicators, probably due to its original partnership with a microfinance institution. Regarding Nubank, despite its remarkable growth in the last years, the company does not affect financial inclusion in Brazil yet. Nonetheless, after its recent expansion to provide more financial services, future research could assess the impact of this company on Brazilian financial inclusion.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".