A data analytics method based on data science and machine learning for bank risk pbkp_rediction in cbkp_redit applications for financial institutions


Abstract:

Nowadays, banks grant cbkp_redits so that customers can acquire a good or service, start or improve a business, among other benefits. The problems that may arise are over-indebtedness and low saving possibilities on the part of customers, so the tendency is the risk of default. Financial institutions require tools for default risk analysis and problem pbkp_rediction. Therefore, in this research, a data analysis method based on data science and machine learning is proposed for bank risk pbkp_rediction in cbkp_redit applications for financial institutions. For the analysis process and for the pbkp_rediction of a cbkp_redit, pbkp_redictive analysis methods are used: Genetic Algorithms (GA), Random Forest (RF), K-Nearest-Neighbor (KNN), Support Vector Machines (SVM) and Neural Network (NN). Quality metrics such as Accuracy, Precision, Recall and F1 Score are used to evaluate the results. A public dataset called Statlog [1] is used. This work opens the door for data analysis in different banking services. The main objective of this research is to help financial companies to optimize their processes.

Año de publicación:

2022

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Análisis de datos
    • Finanzas
    • Ciencias de la computación

    Áreas temáticas:

    • Programación informática, programas, datos, seguridad
    • Economía
    • Métodos informáticos especiales