Abstract
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.
Original language | English |
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Title of host publication | 2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings |
Editors | Monica Andrea Rico Martinez |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728194660 |
DOIs | |
State | Published - 30 Sep 2020 |
Event | 2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - 2020 International Conference on Innovation and Trends in Engineering, CONIITI 2020 - Bogota, Colombia Duration: 30 Sep 2020 → 2 Oct 2020 |
Publication series
Name | 2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings |
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Conference
Conference | 2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - 2020 International Conference on Innovation and Trends in Engineering, CONIITI 2020 |
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Country/Territory | Colombia |
City | Bogota |
Period | 30/09/20 → 2/10/20 |
Bibliographical note
Funding Information:The authors would like to thank the Peruvian University of Applied Sciences for the partial funding of this research.
Publisher Copyright:
© 2020 IEEE.
Keywords
- C4.5
- KDD
- SPSS
- association of savings and credits
- data mining
- marketing