Increasing Digital Activity and Billing in a Banking Entity using Machine Learning

Autores/as

  • Joselin Diestra Pontificia Universidad Católica Del Perú - (Pe), Perú
  • Eduardo Carbajal Pontificia Universidad Católica Del Perú - (Pe), Perú

DOI:

https://doi.org/10.18687/LACCEI2025.1.1.716

Palabras clave:

Machine Learning, Banking, Supervised Models

Resumen

This report describes a project in which Machine Learning technology is used to predict the propensity for e-commerce consumption of a banking entity's customers. The objective of the project is to increase two key business indicators: the percentage of clients who consume digitally in the month and billing, that is, the total amount consumed by clients; To this end, Machine Learning models were developed to predict which customers are the most prone to e-commerce consumption and which are the least prone, thus enabling the business to use this valuable information to redesign, improve and optimize its products. incentive strategies and launch campaigns to clients.

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Publicado

2025-04-09

Número

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Articles

Cómo citar

Diestra, J., & Carbajal, E. (2025). Increasing Digital Activity and Billing in a Banking Entity using Machine Learning. LACCEI, 1(12). https://doi.org/10.18687/LACCEI2025.1.1.716