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Gutierrez, J., Vigo, V. (2021). Modelo de aprendizaje automatizado del proceso de venta de productos financieros en un Call Center [Universidad de Lima]. https://hdl.handle.net/20.500.12724/14344
Gutierrez, J., Vigo, V. Modelo de aprendizaje automatizado del proceso de venta de productos financieros en un Call Center []. PE: Universidad de Lima; 2021. https://hdl.handle.net/20.500.12724/14344
@misc{sunedu/3468078,
title = "Modelo de aprendizaje automatizado del proceso de venta de productos financieros en un Call Center",
author = "Vigo Liñan, Vanessa Stephany",
publisher = "Universidad de Lima",
year = "2021"
}
Title: Modelo de aprendizaje automatizado del proceso de venta de productos financieros en un Call Center
Authors(s): Gutierrez Salas, Jorge Joao; Vigo Liñan, Vanessa Stephany
Advisor(s): Linares Barbero, Manuela
OCDE field: https://purl.org/pe-repo/ocde/ford#2.02.04
Issue Date: 2021
Institution: Universidad de Lima
Abstract: El presente proyecto se enfocó en el diseño, construcción e implementación de un
servicio que realizó la predicción del comportamiento de un potencial cliente, con el fin
de concretar la venta de un producto financiero de manera anticipada, el cual está
basado en machine learning. El prototipo ha sido probado con una base de datos de
clientes de una entidad financiera, a los cuales se les ofreció un producto financiero
como, por ejemplo, un préstamo de libre disponibilidad, tarjetas de crédito, préstamos a
pymes, créditos hipotecarios, créditos vehiculares, etc. y obteniendo como resultado una
venta concretada o una desestimación del ofrecimiento. Con esta información, y a
través de diferentes algoritmos predictivos, se construyó un modelo adecuado que
permita predecir ventas de productos financieros. Los beneficiarios de la solución
implementada serán empresas que brinden servicios de outsourcing (BPO) a entidades
financieras. Estas empresas obtienen utilidades por comisión de venta utilizando
recursos humanos y tecnológicos para lograr concretar ventas. Bajo este esquema, el
modelo predictivo implementado permitió disponibilizar un servicio el cual, al ser
invocado, permita aumentar la probabilidad de venta y a su vez logró optimizar la
operación a nivel de recurso humano, reduciendo la cantidad de ejecutivos de venta, y
aumentando la productividad del área de back office del servicio de outsourcing,
evitando tiempos muertos propios de una operación dependiente de las ventas
concretadas.
El módulo de predicción desarrollado se presentó en una aplicación web que permitió
ingresar los datos de entrada (registro histórico de ventas) y como resultado se mostró
las predicciones basadas en los modelos de machine learning que obtuvieron mejores
resultados. La predicción y su evolución en el tiempo se presentaron en un dashboard
interactivo mostrando los resultados de venta mensuales, campañas de venta, cantidad
de leads, modelos predictivos, efectividad de venta, venta por mes y venta total.
This project focused on the design, construction and implementation of a service that predicted the behavior of a potential client, in order to finalize the sale of a financial product in advance, which is based on machine learning. The prototype has been tested with a database of clients of a financial institution, which were offered a financial product such as, for example, a freely available loan, credit cards, loans to pymes, mortgage loans, vehicle loans, etc. and obtaining as a result a final sale or a rejection of the offer. With this information, and through different predictive algorithms, an adequate model was built to predict sales of financial products. The beneficiaries of the implemented solution will be companies that provide outsourcing services (BPO) to financial entities. These companies obtain profits from sales commission using human and technological resources to achieve sales. Under this scheme, the predictive model implemented made it possible to make a service available which, when invoked, allows to increase the probability of sale and in turn managed to optimize the operation at the human resource level, reducing the number of sales executives, and increasing productivity. of the back office area of the outsourcing service, avoiding downtime typical of an operation dependent on the completed sales. The prediction module developed was presented in a web application that allowed the input data to be entered (historical sales record) and as a result the predictions based on the machine learning models that obtained the best results were shown. The prediction and its evolution over time were presented in an interactive dashboard showing the monthly sales results, sales campaigns, number of leads, predictive models, sales effectiveness, sales per month and total sales.
This project focused on the design, construction and implementation of a service that predicted the behavior of a potential client, in order to finalize the sale of a financial product in advance, which is based on machine learning. The prototype has been tested with a database of clients of a financial institution, which were offered a financial product such as, for example, a freely available loan, credit cards, loans to pymes, mortgage loans, vehicle loans, etc. and obtaining as a result a final sale or a rejection of the offer. With this information, and through different predictive algorithms, an adequate model was built to predict sales of financial products. The beneficiaries of the implemented solution will be companies that provide outsourcing services (BPO) to financial entities. These companies obtain profits from sales commission using human and technological resources to achieve sales. Under this scheme, the predictive model implemented made it possible to make a service available which, when invoked, allows to increase the probability of sale and in turn managed to optimize the operation at the human resource level, reducing the number of sales executives, and increasing productivity. of the back office area of the outsourcing service, avoiding downtime typical of an operation dependent on the completed sales. The prediction module developed was presented in a web application that allowed the input data to be entered (historical sales record) and as a result the predictions based on the machine learning models that obtained the best results were shown. The prediction and its evolution over time were presented in an interactive dashboard showing the monthly sales results, sales campaigns, number of leads, predictive models, sales effectiveness, sales per month and total sales.
Link to repository: https://hdl.handle.net/20.500.12724/14344
Discipline: Ingeniería de Sistemas
Grade or title grantor: Universidad de Lima. Facultad de Ingeniería y Arquitectura
Grade or title: Ingeniero de Sistemas
Juror: Matuk Chijner, Andrea; Huaynate Mato, Edwin David; Pachas Chura, Carlos Eduardo; Linares Barbero, Manuela
Register date: 22-Oct-2021
This item is licensed under a Creative Commons License