TY - JOUR
T1 - Predicting financial inclusion in Peru
T2 - Application of machine learning algorithms
AU - Maehara, Rocío
AU - Sánchez, Luis Enrique Benites
AU - Talavera, Alvaro
AU - Aybar-Flores, Alejandro
AU - Muñoz, Miguel
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - Correlación
KW - Crecimiento
KW - Financiamiento de empresas
KW - Manufactura
KW - Perú
KW - Correlation
KW - Growth
KW - Financing companies
KW - Manufacturing
KW - Peru
KW - generalized linear models
KW - Shapley values
KW - financial inclusion
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85183357133&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/4c99dc39-28d1-38e6-9fd2-0c9f19db8e13/
U2 - 10.3390/jrfm17010034
DO - 10.3390/jrfm17010034
M3 - Article in a journal
SN - 1911-8066
VL - 17
JO - Journal of Risk and Financial Management
JF - Journal of Risk and Financial Management
IS - 1
M1 - 34
ER -
