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

• Javier Gamboa-Cruzado ^[1] ; Ronald E. Astudillo-Aquino ^[4] ; Dennis Castro-Valverde ^[2] ; Jorge Nolasco-Valenzuela ^[4] ; Juan Gamarra Moreno ^[1] ; Guido Larico Uchamaco ^[5] ; Flavio Amayo-Gamboa ^[3]
  1. [1] Universidad Nacional Mayor de San Marcos

     Universidad Nacional Mayor de San Marcos

     Perú

     
  2. [2] Universidad Peruana de Ciencias Aplicadas

     Universidad Peruana de Ciencias Aplicadas

     Perú

     
  3. [3] Universidad Nacional de Trujillo

     Universidad Nacional de Trujillo

     Provincia de Trujillo, Perú

     
  4. [4] Facultad de Ingeniería Industrial y de Sistemas, Universidad Nacional Federico Villareal, Lima, Perú
  5. [5] Facultad de Ingeniería, Universidad Nacional de Cañete, Lima, Perú

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• Localización: RISTI: Revista Ibérica de Sistemas e Tecnologias de Informação, ISSN-e 1646-9895, Nº. Extra 72, 2024, págs. 451-470
• Idioma: español
• Títulos paralelos:
  • Influence of Machine Learning on Credit Risk Assessment in Financial Institutions: A Comprehensive Systematic and Bibliometric Review
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• Resumen
  • español

    El uso del Machine Learning (ML) en bancos ha ganado impulso debido a su precisión predictiva utilizando algoritmos. Este estudio se centra en determinar la influencia del ML en la evaluación del riesgo crediticio en el sector financiero. Se realizó una revisión sistemática de artículos de 2017 a 2022, consultando fuentes como ACM Digital Library, IEEE Xplore, Scopus, entre otras. Se aplicaron rigurosos criterios de exclusión y calidad, resultando en la selección de 62 artículos para análisis. Los hallazgos muestran una variedad de enfoques e innovaciones en el uso de ML para evaluar riesgos crediticios. La investigación documenta en detalle las metodologías y técnicas de ML más relevantes para prevenir el riesgo crediticio, proporcionando datos cruciales para las instituciones financieras. Este conocimiento permite a los bancos mejorar sus metodologías de evaluación de riesgo, optimizando sus procesos y mejorando la toma de decisiones basada en datos. Estos resultados enfatizan la creciente relevancia del ML en las finanzas y su potencial para transformar prácticas tradicionales, fortaleciendo la seguridad y la confiabilidad en las operaciones bancarias.

  • English

    The use of Machine Learning (ML) in banks has gained momentum due to its predictive accuracy using algorithms. This study focuses on determining the influence of ML on credit risk assessment in the financial sector. A systematic review of articles from 2017 to 2022 was conducted, consulting sources such as ACM Digital Library, IEEE Xplore, Scopus, among others. Rigorous exclusion and quality criteria were applied, resulting in the selection of 62 articles for analysis. The findings reveal a variety of approaches and innovations in the use of ML to assess credit risks. The research documents in detail the most relevant ML methodologies and techniques to prevent credit risk, providing crucial data for financial institutions. This knowledge allows banks to improve their risk assessment methodologies, optimizing their processes and enhancing data-based decisionmaking. These results emphasize the growing relevance of ML in finance and its potential to transform traditional practices, strengthening security and reliability in banking operations.

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