Feature Distribution and Co-occurrence Patterns in Public Credit Decision Datasets

• André S. G. Moreira UFRN
• Caio V. A. Justino UFRN
• Igor W. C. Fernandes UFRN
• Marcos Murguel ICT Itaú
• Luana Oliveira ICT Itaú
• Anna M. C. Araujo Itaú-Unibanco S.A.
• Luana F. Alvarenga UFRN
• Renan C. Moioli UFRN
• Cesar Renno-Costa UFRN

DOI: https://doi.org/10.5753/eniac.2026.31312

Resumo

Automated credit scoring relies heavily on machine learning, yet the specific variables driving these models remain obscured by proprietary restrictions, raising significant ethical and regulatory concerns. This study systematically analyzes 51 public credit decision datasets to evaluate feature diversity, distribution, and co-occurrence patterns. Adapting PRISMA guidelines for dataset selection, we employ a large language model to taxonomically categorize features. Our results suggest a prevalent use of financial and sociodemographic variables, particularly age and income, pointing to a potential tension between the pursuit of predictive accuracy and data minimization mandates, such as GDPR and LGPD.

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