A Machine Learning approach for increasing user engagement in the Fintech Industry

Is it possible to make a user become a regular operator of an application? Make him feel called to use it naturally, as one more task in his daily life? In this thesis, we seek to respond to this not so trivial concern by using Machine Learning as a support tool for the development of two solutio...

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Detalles Bibliográficos
Autor principal: Lo Sasso, Emiliano
Otros Autores: Maingard, Nadja
Formato: Tesis de maestría acceptedVersion
Lenguaje:Inglés
Publicado: 2023
Materias:
Consumer behavior
Artificial Intelligence
Machine Learning
Fintech
Markovian Processes
Acceso en línea:https://repositorio.utdt.edu/handle/20.500.13098/11580
Aporte de:
Repositorio Digital Universidad Torcuato Di Tella de Universidad Torcuato Di Tella
id I57-R163-20.500.13098-11580
record_format dspace
institution Universidad Torcuato Di Tella
institution_str I-57
repository_str R-163
collection Repositorio Digital Universidad Torcuato Di Tella
language Inglés
orig_language_str_mv eng
topic Consumer behavior
Artificial Intelligence
Machine Learning
Fintech
Markovian Processes
spellingShingle Consumer behavior
Artificial Intelligence
Machine Learning
Fintech
Markovian Processes
Lo Sasso, Emiliano
A Machine Learning approach for increasing user engagement in the Fintech Industry
topic_facet Consumer behavior
Artificial Intelligence
Machine Learning
Fintech
Markovian Processes
description Is it possible to make a user become a regular operator of an application? Make him feel called to use it naturally, as one more task in his daily life? In this thesis, we seek to respond to this not so trivial concern by using Machine Learning as a support tool for the development of two solutions that allow the user to get more engaged. As part of a project within a Marketing team of a Fintech company, we seek to help users go from installing the app to the state defined as ”Habit”. To achieve this, we take advantage of the available data to develop two Artificial Intelligence models based on recommendation systems that seek to find the action within the application that has the greatest chance of being chosen by him. In the course of this work, some basic concepts (and others not so much) neces- sary to understand both the business aspects and those related to the more technical aspect will be introduced. As a final result, we have developed two models whose objective is to suggest the next most favorable action for the user, that is, the one that he would not do by himself but because it was recommended. Always in pursuit of getting the user to reach the state of Habit. The first of them, a model based on Markovian Processes, exploits the concept of the Transition Matrix to determine through it the proba- bility that a person moves from one state (or operation) to another. The second of the solutions, based on machine learning techniques, seeks to find incremental suggestions through an Uplift model that determines those actions that are most likely to generate a positive impact on the user. With this, we hope to improve the number of users who reach the status of Habit with respect to current initiatives, thus achieving more committed users and of greater value to the company, without neglecting their experience or their interests.
author2 Maingard, Nadja
author_facet Maingard, Nadja
Lo Sasso, Emiliano
format Tesis de maestría
acceptedVersion
author Lo Sasso, Emiliano
author_sort Lo Sasso, Emiliano
title A Machine Learning approach for increasing user engagement in the Fintech Industry
title_short A Machine Learning approach for increasing user engagement in the Fintech Industry
title_full A Machine Learning approach for increasing user engagement in the Fintech Industry
title_fullStr A Machine Learning approach for increasing user engagement in the Fintech Industry
title_full_unstemmed A Machine Learning approach for increasing user engagement in the Fintech Industry
title_sort machine learning approach for increasing user engagement in the fintech industry
publishDate 2023
url https://repositorio.utdt.edu/handle/20.500.13098/11580
work_keys_str_mv AT losassoemiliano amachinelearningapproachforincreasinguserengagementinthefintechindustry
AT losassoemiliano machinelearningapproachforincreasinguserengagementinthefintechindustry
bdutipo_str Repositorios
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