A machine learning approach for banks classification and forecast

Fontalvo Herrera, TomasDe La Hoz Dominguez, Enrique2023-07-142023-07-142019-042023-07https://hdl.handle.net/20.500.12585/12096n this research, a classification model is developed for the banking sector using the machine earning technique GLMNET. In the first place, a clustering process was developed, where 3 clearly differentiated groups were found. Subsequently, a Fuzzy analysis was performed finding the probabilities of transition of the banks to each group found, finally, the GLMNET algorithm was implemented, the automatic classification of the banks according to their financial items, obtaining a result of 95% accuracy. © 2019 International Business Information Management Association (IBIMA).10 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/A machine learning approach for banks classification and forecastinfo:eu-repo/semantics/articleCustomer Churn;Sales;Customer Relationship Managementinfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternacionalUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarLEMB