Influencia de Machine Learning en la Evaluación de Riesgo Crediticio en Entidades Financieras: Una exhaustiva Revisión Sistemática y Bibliométrica

Rigorous exclusion and quality criteria were applied, resulting in the selection of 62 articles for analysis. The research documents in detail the most relevant ML methodologies and techniques to prevent credit risk, providing crucial data for financial institutions. Keywords: Machine Learning; Cred...

Full description

Saved in:
Bibliographic Details
Published inRISTI : Revista Ibérica de Sistemas e Tecnologias de Informação no. E72; pp. 451 - 470
Main Authors Gamboa-Cruzado, Javier, Astudillo-Aquino, Ronald E, Castro-Valverde, Dennis, Nolasco-Valenzuela, Jorge, Moreno, Juan Gamarra, Uchamaco, Guido Larico, Amayo-Gamboa, Flavio
Format Journal Article
LanguageSpanish
Published Lousada Associação Ibérica de Sistemas e Tecnologias de Informacao 01.08.2024
Subjects
Algorithms
Bibliometrics
Big Data
Credit risk
Digital libraries
Feature selection
Financial institutions
Financial management
Internet of Things
Literature reviews
Machine learning
Risk assessment
Online AccessGet full text

Cover

More Information
Summary:Rigorous exclusion and quality criteria were applied, resulting in the selection of 62 articles for analysis. The research documents in detail the most relevant ML methodologies and techniques to prevent credit risk, providing crucial data for financial institutions. Keywords: Machine Learning; Credit Risk; Financial Institutions; Algorithms; Systematic Literature Review. 1. Asimismo, Bi y Liang (2022), en su estudio "Risk Assessment of Operator's Big Data Internet of Things Credit Financial Management Based on Machine Learning", subrayan la crucial importancia de la innovación en la evaluación del riesgo crediticio para las instituciones financieras.
ISSN:1646-9895