Predicting fraud behaviour in online betting

Detalhes bibliográficos
Autor(a) principal: Tedim, Margarida de Sousa
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/59929
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
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spelling Predicting fraud behaviour in online bettingOnline FraudBetting MarketData MiningMachine LearningPortugalProject Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementFraud isn’t a new issue, there are discussions about this matter since the beginning of commerce. With the advance of the Internet this technique gained strain and became a billion-dollar business. There are many different types of online financial fraud: account takeover; identity theft; chargeback; credit card transactions; etc. Online betting is one of the markets where fraud is increasing every day. In Portugal, the regulation of gambling and online betting was approved in April 2015. In one hand, this legislation made possible the exploration of this business in a controlled and regulated environment, but on the other hand it encouraged customers to develop new ways of fraud. Traditional data analysis used to detect fraud involved different domains such as economics, finance and law. The complexity of these investigations soon became obsolete. Being fraud an adaptive crime, different areas such as Data Mining and Machine Learning were developed to identify and prevent fraud. The main goal of this Project is to develop a predicting model, using a data mining approach and machine learning methods, able to identify and prevent online financial fraud on the Portuguese Betting Market, a new but already strong business.Henriques, Roberto André PereiraRUNTedim, Margarida de Sousa2019-02-08T14:42:50Z2019-01-182019-01-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/59929TID:202167518enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:28:44Zoai:run.unl.pt:10362/59929Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:27.315994Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting fraud behaviour in online betting
title Predicting fraud behaviour in online betting
spellingShingle Predicting fraud behaviour in online betting
Tedim, Margarida de Sousa
Online Fraud
Betting Market
Data Mining
Machine Learning
Portugal
title_short Predicting fraud behaviour in online betting
title_full Predicting fraud behaviour in online betting
title_fullStr Predicting fraud behaviour in online betting
title_full_unstemmed Predicting fraud behaviour in online betting
title_sort Predicting fraud behaviour in online betting
author Tedim, Margarida de Sousa
author_facet Tedim, Margarida de Sousa
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Tedim, Margarida de Sousa
dc.subject.por.fl_str_mv Online Fraud
Betting Market
Data Mining
Machine Learning
Portugal
topic Online Fraud
Betting Market
Data Mining
Machine Learning
Portugal
description Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
publishDate 2019
dc.date.none.fl_str_mv 2019-02-08T14:42:50Z
2019-01-18
2019-01-18T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/59929
TID:202167518
url http://hdl.handle.net/10362/59929
identifier_str_mv TID:202167518
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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