Prediction of financial product acquisition for Peruvian savings and credit associations

Emmanuel Roque Vargas, Ricardo Cadillo Montesinos, David Mauricio

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings
EditorsMonica Andrea Rico Martinez
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194660
DOIs
StatePublished - 30 Sep 2020
Event2020 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 20202 Oct 2020

Publication series

Name2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings

Conference

Conference2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - 2020 International Conference on Innovation and Trends in Engineering, CONIITI 2020
CountryColombia
CityBogota
Period30/09/202/10/20

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.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • C4.5
  • KDD
  • SPSS
  • association of savings and credits
  • data mining
  • marketing

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