Credit granting by the national financial corporation: an exploratory data analysis (EDA).

Authors

  • Joseph Paúl Delgado Quijije Universidad Laica Eloy Alfaro de Manabí ULEAM
  • Luis Cedeño-Valarezo Escuela Superior Politécnica Agropecuaria de Manabí Manuel Félix López

DOI:

https://doi.org/10.56124/encriptar.v8i16.009

Keywords:

Concesión, Créditos CFN, EDA

Abstract

This study examined the credit allocation granted by the Corporación Financiera Nacional (CFN) in Ecuador, considering its role as a public financial institution aimed at fostering productive sectors. The objective was to analyze the behavior of granted loans between 2022 and 2024 through exploratory data analysis (EDA) techniques. A quantitative, descriptive, and cross-sectional approach was applied, using secondary data retrieved from CFN’s open data platform. The dataset included quantitative variables (loan amount, number of operations) and categorical variables (type of credit, operation type, loan status, and province). The analysis was conducted using RStudio and tidyverse packages, including data cleaning, univariate and bivariate analysis, and visualization techniques. The results revealed that most of the loans granted were under USD 10,000, though notable outliers were present. An inverse relationship was identified between loan amounts and the number of operations, indicating that higher-value loans were granted to fewer beneficiaries. The provinces of Guayas and Pichincha concentrated the majority of credit operations, suggesting a geographic bias in credit distribution. Moreover, the “credit” product and “original” loan status were the most prevalent, both with high variability. It was concluded that CFN’s financial resources were channeled through a concentrated pattern across specific products and regions, raising concerns about the equity and redistributive reach of public financing.

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Published

2025-10-24

How to Cite

Delgado Quijije, J. P., & Cedeño-Valarezo, L. . (2025). Credit granting by the national financial corporation: an exploratory data analysis (EDA) . Scientific Journal of Informatics ENCRYPT - ISSN: 2737-6389., 8(16), 169–186. https://doi.org/10.56124/encriptar.v8i16.009