Palabras clave
Riesgo crediticio
Aprendizaje automático
Modelos predictivos
Regulación financiera
Cómo citar
Resumen
Este artículo es una revisión sistemática de la literatura sobre la aplicación de la inteligencia artificial (IA) en
el sector financiero, con énfasis en la evaluación del riesgo crediticio. Para ello, se recopilan y analizan 51
trabajos de investigación publicados en bases de datos académicas internacionales, seleccionados según
criterios de relevancia, accesibilidad y calidad de citación. El estudio aborda tanto los métodos tradicionales
de análisis crediticio, basados en modelos estadísticos y reglas determinísticas, como los enfoques modernos
de aprendizaje automático (ML), que incorporan técnicas computacionales avanzadas para mejorar la
predicción y clasificación del riesgo. También se incluye investigación complementaria sobre marcos
regulatorios, implicaciones éticas, el impacto ambiental de la IA y su relación con la transformación digital del
sector financiero en el contexto de la Industria 4.0.
Los resultados muestran que los modelos de ML tienden a superar a los métodos tradicionales en términos de
precisión y capacidad predictiva, aunque la implementación de algunos de ellos enfrenta limitaciones asociadas
con la explicabilidad, la transparencia algorítmica y el cumplimiento normativo. La integración efectiva de la
IA en la evaluación del riesgo crediticio también requiere un equilibrio entre la innovación tecnológica y la
regulación responsable, lo que podría favorecer la adopción de modelos híbridos que combinen la solidez
analítica de los métodos tradicionales con la eficiencia predictiva de los modelos de ML.
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