Abstract
This study evaluates financial inclusion in Peru through self-organizing maps. Financial inclusion is a multidimensional issue of great importance on the global agenda and continues to concern various actors internationally. In this context, the objective is to assess the financial inclusion situation in the country and determine how self-organizing maps can complement standard models for this purpose. The empirical aim is to demonstrate how this technique can help identify priority areas and vulnerable groups, thus facilitating decision-making and policy design to improve the access to and use of financial services among Peruvian consumers by finding clearly defined profiles that allow the identification of potential problems within each category. This makes it possible to create customized strategies for each group, such as addressing the financial inclusion barriers faced by rural residents, compounded by low income and educational levels.
| Original language | English |
|---|---|
| Article number | 549 |
| Journal | Journal of Risk and Financial Management |
| Volume | 17 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2024 |
Bibliographical note
Publisher Copyright:© 2024 by the authors.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth
Keywords
- clustering
- financial inclusion
- machine learning
- self-organizing maps
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