Machine learning for personal credit evaluation: A systematic review

• Autores: Ogosi Auqui, José Antonio, Cano Chuqui, Jorge, Guadalupe Mori, Victor Hugo, Obando Pacheco, David Hugo
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/5917
• Enlace del recurso: https://hdl.handle.net/20.500.12867/5917; http://doi.org/10.37394/232018.2022.10.9
• Nivel de acceso: acceso abierto
• Materia: Machine learning; Risk assessment (Finances); Artificial intelligence; https://purl.org/pe-repo/ocde/ford#2.02.03

The importance of information in today's world as it is a key asset for business growth and innovation. The problem that arises is the lack of understanding of knowledge quality properties, which leads to the development of inefficient knowledge-intensive systems. But knowledge cannot be shared effectively without effective knowledge-intensive systems. Given this situation, the authors must analyze the benefits and believe that machine learning can benefit knowledge management and that machine learning algorithms can further improve knowledge-intensive systems. It also shows that machine learning is very helpful from a practical point of view. Machine learning not only improves knowledge-intensive systems but has powerful theoretical and practical implementations that can open up new areas of research. The objective set out is the comprehensive and systematic literature review of research published between 2018 and 2022, these studies were extracted from several critically important academic sources, with a total of 73 short articles selected. The findings also open up possible research areas for machine learning in knowledge management to generate a competitive advantage in financial institutions.