Publication
Using Machine Learning to classify responsible from non-responsible online gamblers
| dc.contributor.advisor | Pereira, Isabel Cecília Correia da Silva Praça Gomes | |
| dc.contributor.author | Neves, Diogo Mota | |
| dc.date.accessioned | 2023-07-31T08:26:15Z | |
| dc.date.embargo | 2026-07-17 | |
| dc.date.issued | 2023 | |
| dc.description.abstract | In recent years, responsible gaming (RG) has become increasingly important to companies whose main activity relies on betting or casino activities and, as more and more people have begun to gamble online. With the proliferation of online gambling, it has become easier for individuals to access gambling sites and place bets from the comfort of their own homes, which can increase the risk of non-responsible gaming practices. Betting companies, such as Betano, that prioritize responsible gaming are more likely to attract and retain customers, as individuals are more likely to trust and feel comfortable using a platform that takes steps to ensure that their gambling habits are safe and controlled. In addition to the business benefits, responsible gaming is also important from a social and ethical perspective. Gambling addiction can have serious consequences for individuals and their families, including financial, relationship, and mental health problems. By taking steps to promote responsible gaming, Betano can help to minimize the negative impacts of gambling and contribute to the well-being of its customers. This research paper will address the issue of responsible gaming for Betano by exploring the use of artificial intelligence (AI) and machine learning (ML) techniques to detect problematic gambling behaviors. By utilizing AI and ML, Betano can better understand and predict gambling behaviors and take proactive steps to promote responsible gaming. This project will analyze the potential benefits and limitations of using these technologies in the context of responsible gaming. | pt_PT |
| dc.description.abstract | O tópico de jogo responsável teve uma crescente importância nas empresas cuja atividade principal anda em torno do jogo de casino e apostas desportivas, principalmente pelo facto de cada vez mais apostadores utilizarem os meios eletrónicos para o fazer confortavelmente nas suas casas. Este aumento de procura das apostas desportivas e casino online levou também ao aumento do número de casos de jogo não responsável. No entanto, as empresas, como a Betano, dá uma grande prioridade e especial atenção ao cumprimento das normas estabelecidas, não só para a comunidade se sentir mais segura enquanto aposta, mas também para reforçar bons hábitos de jogo e não criar dependência que poderá ser fatal para muitas famílias. Este projeto aqui apresentado irá analisar esta área de jogo responsável, quais os seus componentes e propor um algoritmo usando aprendizagem máquina para detetar padrões problemáticos em indivíduos. Ao analisarmos todos os casos de forma automática, a Betano consegue de melhor forma prever e tomar ações até antes dos casos se tornarem graves e sobretudo promover o jogo responsável. | pt_PT |
| dc.identifier.tid | 203335171 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/23386 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Non-Responsible Gaming | pt_PT |
| dc.subject | Responsible Gaming | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Artificial Intelligence | pt_PT |
| dc.subject | Gambling Problems | pt_PT |
| dc.subject | Gaming Problems | pt_PT |
| dc.title | Using Machine Learning to classify responsible from non-responsible online gamblers | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Engenharia de Inteligência Artificial | pt_PT |