ETHICAL AI IN CREDIT RISK MANAGEMENT: A SCOPUS-BASED SCIENCE-MAPPING STUDY (2005–2025)

Objetivo: This study maps Scopus-indexed research on ethical algorithms in credit risk management from 2005 to 2025. Métodos: A bibliometric science-mapping design was applied using Bibliometrix/Biblioshiny in R. The Scopus search, conducted on 8 March 2026, retrieved 242 records; after removing duplicates and incomplete metadata, 219 documents formed the final corpus. Resultados: The analysis examined publication growth, impact, keyword co-occurrence, and collaboration patterns. Findings show rapid expansion, with a 25.89% annual growth rate, and dissemination across journals and conference proceedings, including IEEE Access, Proceedings of the ACM on Human-Computer Interaction, CEUR Workshop Proceedings, and Lecture Notes in Networks and Systems. Key themes include machine learning, artificial intelligence, decision-making, fairness, and transparency. Conceptual mapping reveals three fronts: algorithmic governance, explainability in credit scoring, and emerging ESG and ethical risk-culture perspectives. Conclusiones: The field remains technically oriented and requires stronger regulatory alignment.