Evaluating Financial Inclusion in Peru: A Cluster Analysis Using Self-Organizing Maps

Alvaro Talavera (), Rocío Maehara, Luis Benites, Benjamin Arriaga and Alejandro Aybar-Flores
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Alvaro Talavera: Departamento de Ingeniería, Universidad del Pacífico, Lima 15072, Peru
Rocío Maehara: Departamento de Ingeniería, Universidad del Pacífico, Lima 15072, Peru
Luis Benites: Departamento de Administración, Universidad del Pacífico, Lima 15072, Peru
Benjamin Arriaga: Departamento de Ingeniería, Universidad del Pacífico, Lima 15072, Peru
Alejandro Aybar-Flores: Departamento de Ingeniería, Universidad del Pacífico, Lima 15072, Peru

JRFM, 2024, vol. 17, issue 12, 1-20

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.

Keywords: financial inclusion; self-organizing maps; clustering; machine learning (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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