Evaluating financial inclusion in Peru: A cluster analysis using self-organizing maps

Alvaro Talavera, Rocío Maehara, Luis Benites, Benjamin Arriaga, Alejandro Aybar-Flores

Research output: Contribution to journalArticle in a journalpeer-review

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 languageEnglish
Article number549
JournalJournal of Risk and Financial Management
Volume17
Issue number12
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Keywords

  • clustering
  • financial inclusion
  • machine learning
  • self-organizing maps

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