TY - GEN
T1 - A Two-Stage Dimension Reduction and Clustering Framework for Financial Behavior and Socio-Demographic Profiling
AU - Aybar-Flores, Alejandro
AU - Maehara, Rocío
AU - Benites, Luis
AU - Muñoz, Miguel
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Financial inclusion (FI) is a critical component of global financial advancement. Despite technological progress, about 1.5 billion people in emerging economies lack access to formal financial systems. Understanding financial decision-making behaviors and preferences is essential for enhancing comprehension of financial inclusion. This study focuses on Peru, a country with relatively low FI levels. Using data from the 2019 National Survey of Demand for Financial Services and Financial Literacy (NSDFS), we implemented a two-stage clustering methodology with dimension reduction techniques and clustering algorithms to uncover social profiles within the Peruvian population. Our findings identified three clusters based on financial behaviors and socio-demographic characteristics. The optimal configuration, utilizing Isomap reduced to 2 dimensions combined with the K-means++ algorithm, achieved a mean aggregated score of 0.832, yielding the best results among the other dimension reduction and clustering techniques considered. The clusters highlighted disparities in financial access, emphasizing the need for targeted interventions. These insights can aid policymakers and regulators in developing strategies to enhance FI in Peru, underscoring the value of clustering techniques in addressing financial inclusion challenges.
AB - Financial inclusion (FI) is a critical component of global financial advancement. Despite technological progress, about 1.5 billion people in emerging economies lack access to formal financial systems. Understanding financial decision-making behaviors and preferences is essential for enhancing comprehension of financial inclusion. This study focuses on Peru, a country with relatively low FI levels. Using data from the 2019 National Survey of Demand for Financial Services and Financial Literacy (NSDFS), we implemented a two-stage clustering methodology with dimension reduction techniques and clustering algorithms to uncover social profiles within the Peruvian population. Our findings identified three clusters based on financial behaviors and socio-demographic characteristics. The optimal configuration, utilizing Isomap reduced to 2 dimensions combined with the K-means++ algorithm, achieved a mean aggregated score of 0.832, yielding the best results among the other dimension reduction and clustering techniques considered. The clusters highlighted disparities in financial access, emphasizing the need for targeted interventions. These insights can aid policymakers and regulators in developing strategies to enhance FI in Peru, underscoring the value of clustering techniques in addressing financial inclusion challenges.
KW - Clustering
KW - Dimension Reduction
KW - Financial Behaviour
KW - Peru
KW - Socio-demographic Characteristics
KW - Unsupervised Learning
UR - https://www.scopus.com/pages/publications/105015300077
U2 - 10.1007/978-3-031-96798-6_27
DO - 10.1007/978-3-031-96798-6_27
M3 - Conference contribution
AN - SCOPUS:105015300077
SN - 9783031967979
T3 - Lecture Notes in Networks and Systems
SP - 335
EP - 351
BT - Artificial Intelligence for System Oriented Design - Proceedings of 8th Computational Methods in Systems and Software, 2024
A2 - Silhavy, Radek
A2 - Silhavy, Petr
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Conference on Computational Methods in Systems and Software, CoMeSySo 2024
Y2 - 12 October 2024 through 14 October 2024
ER -
