Calculando el riesgo de insolvencia, de los métodos tradicionales a las redes neuronales artificiales. Una revisión de literatura
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En la administración financiera de toda organización el cálculo del riesgo de insolvencia se ha convertido en un parámetro importante, buscando anticiparse a la eventualidad de llegar a tener un problema económico y generar insolvencia. El objetivo de este trabajo es determinar si en el cálculo del riesgo de insolvencia, el uso de redes neuronales artificiales genera mejores resultados que las metodologías tradicionales, buscando las principales características dentro de las aplicaciones realizadas por distintos autores a través del tiempo. De esta manera se observan las principales variables que pueden evidenciar que la aplicación de la metodología de redes neuronales facilita el cálculo del riesgo de insolvencia. A través de la revisión bibliográfica, en el período 1992-2021, con el uso del método analítico-sintético se puede evidenciar que el modelo expuesto es considerado como eficiente según sus resultados, con ajustes que, en la mayoría de los casos expuestos, superan el 80% de eficacia. Los resultados encontrados permitieron concluir que la estructura básica de una red neuronal viene dada por tres capas: una de entrada, una de salida y una oculta. Sin embargo, el número de nodos es el que varía en cada una de las aplicaciones realizadas por los distintos autores, dado que los mismos representan a las variables, en este caso indicadores financieros más relevantes según la aplicación planteada. Finalmente se logró evidenciar cuáles son los indicadores financieros más usados en las distintas aplicaciones de redes neuronales. Todo indica que las redes neuronales generan resultados más efectivos que los métodos tradicionales.
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