Data Mining for the Adjustment of Credit Scoring Models in Solidarity Economy Entities: A Methodology for Addressing Class Imbalances

Ivan Mauricio Bermudez Vera (), Jaime Mosquera Restrepo and Diego Fernando Manotas-Duque
Additional contact information
Ivan Mauricio Bermudez Vera: School of Industrial Engineering, Universidad del Valle, Cali 760042, Colombia
Jaime Mosquera Restrepo: School of Statistics, Universidad del Valle, Cali 760042, Colombia
Diego Fernando Manotas-Duque: School of Industrial Engineering, Universidad del Valle, Cali 760042, Colombia

Risks, 2025, vol. 13, issue 2, 1-16

Abstract: This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a “default” in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk management by incorporating internal and external data to assess default risk. Data mining techniques are applied to address class imbalances, redefining the term “default” to include external credit information and increasing the representation of the minority class. The effectiveness of machine learning and statistical models is evaluated using class-balancing methods such as under-sampling, over-sampling, and the Synthetic Minority Over-sampling Technique (SMOTE). The evaluation is based on the Balanced Accuracy metric and the holding power of the performance, ensuring a consistent predictive power of the model while avoiding overfitting. While machine learning methods can improve credit scoring, logistic regression-based models remain effective, especially when combined with class-balancing techniques. It is concluded that a balanced sample in a class size is essential to improve predictive performance.

Keywords: credit risk; solidarity economy; data mining; class balancing; logistic regression (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/13/2/20/pdf (application/pdf)
https://www.mdpi.com/2227-9091/13/2/20/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:13:y:2025:i:2:p:20-:d:1573147

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().