Predicting Financial Inclusion in Peru: Application of Machine Learning Algorithms

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

JRFM, 2024, vol. 17, issue 1, 1-25

Abstract: Financial inclusion is a fundamental and multidimensional matter that has acquired importance on the global agenda in recent years. In addition, it is still a source of great interest and concern for lawmakers, international organizations, scholars, and financial institutions worldwide. In that regard, this research focuses on Peru to assess the country’s financial inclusion condition, which continues to face significant hurdles in providing financial services to its whole population despite economic improvement. The aim of this article is twofold, based on recent data on demand for financial services and financial culture in the country: (1) to empirically test how machine learning methods, such as decision trees, random forests, artificial neural networks, XGBoost, and support vector machines, can be a valuable complement to standard models (i.e., generalized linear models like logistic regression) for assessing financial inclusion in Peru, and (2) to identify the most influential sociodemographic factors on financial inclusion assessment in the country. The results may catalyze the integration of machine learning techniques into the Peruvian financial system, garnering the interest of finance researchers and policymakers committed to augmenting financial access and utilization among Peruvian consumers.

Keywords: financial inclusion; generalized linear models; machine learning; Shapley values; Peru (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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