Predictive models based on machine learning to analyze the adoption of digital payments in Latin America and the Caribbean
Bibliographic record
Abstract
The use of technology in the financial industry has experienced sustained growth in recent years. However, in many emerging economies, a significant proportion of the population still does not utilize digital solutions for financial transactions. Promoting financial inclusion through digital environments is essential for driving social and economic development. This study aims to develop machine learning models to predict the adoption of digital payments in Latin America and the Caribbean using statistical data from the World Bank's Global Findex Database for 2021. The performance of the Random Forest, LightGBM, XGBoost, and CatBoost algorithms was compared, with the optimal hyperparameter combination identified through Bayesian optimization. The results show that LightGBM achieved the highest performance in predicting digital payments, with an F1-score of 90.25% and a more stable balance between precision and recall compared to the other models. These findings highlight the value of machine learning models in the financial sector, as they enable a more accurate identification of users adopting digital solutions, facilitating the design of strategies to strengthen financial inclusion in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".