El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Savings and credit cooperatives in Peru are of great importance for their participation in the economy, reaching in 2019, deposits and deposits and assets of more than 2,890,191,000. However, they do not invest in predictive technologies to identify customers with a higher probability of purchasing a financial product, making marketing campaigns unproductive. In this work, a model based on machine learning is proposed to identify the clients who are most likely to acquire a financial product for Peruvian savings and credit cooperatives. The model was implemented using IBM SPSS Modeler for predictive analysis and tests were performed on 40,000 records on 10,000 clients, obtaining 91.25% accuracy on data not used in training. / Revisión por pares
| Identifer | oai:union.ndltd.org:PERUUPC/oai:repositorioacademico.upc.edu.pe:10757/656581 |
| Date | 30 September 2020 |
| Creators | Vargas, Emmanuel Roque, Cadillo Montesinos, Ricardo, Mauricio, David |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Source Sets | Universidad Peruana de Ciencias Aplicadas (UPC) |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/article, info:eu-repo/semantics/article |
| Format | application/html |
| Source | Repositorio Academico - UPC, Universidad Peruana de Ciencias Aplicadas (UPC), 2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings |
| Rights | info:eu-repo/semantics/embargoedAccess |
| Relation | https://ieeexplore.ieee.org/document/9240413 |
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