Case study: an analytical model for the fraud detection In company purchases

Detalhes bibliográficos
Autor(a) principal: Reynaud, Raquel Alexandra Batista
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/103106
Resumo: Dissertation 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 Case study: an analytical model for the fraud detection In company purchasesFraud DetectionRiskAnalytical ModelsCompany PurchasesAssuranceDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementIn a world where the business landscape is changing as a consequence of the increasing importance of the digital area, the financial and reporting environment are also being reshaped, resulting in several challenges for audit committees and auditors. Assurance services are responsible for transmitting clear information and nowadays auditing has become an increasingly demanding task. In this regard, detection fraud is one of the areas explored by assurance, including studying the risk of companies committing fraud, and also the hypothesis of employees committing these kinds of misconduct. In fact, in the modern digital world, it becomes easier to commit fraud, therefore it is quite relevant to study its impacts and causes. Theories related to the fraud triangle as well as fraud related to the utilities sector will be explored and will be the basis of this study. Having this in mind, this study aims to identify the most significant variables in detecting the risk of fraud in company purchases, using some companies’ data in order to help this analysis. Having this in mind, the conclusions about the selected model were to consider two different approaches: Keeping All Variables and Removing Some Variables. For the first option, the best model was Linear Regression and for the other one was Neural Networks, considering Misclassification Rate and Captured Response as significant statistics. Thus, this study aims to fill the gap of information and studies in this area by providing relevant inputs that may be used on other studies in this field.Gonçalves, Rui Alexandre HenriquesRUNReynaud, Raquel Alexandra Batista2020-08-31T16:57:24Z2020-07-162020-07-16T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/103106TID:202512002enginfo: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:RCAAP2024-03-11T04:48:33Zoai:run.unl.pt:10362/103106Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:48.278615Repositório 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 Case study: an analytical model for the fraud detection In company purchases
title Case study: an analytical model for the fraud detection In company purchases
spellingShingle Case study: an analytical model for the fraud detection In company purchases
Reynaud, Raquel Alexandra Batista
Fraud Detection
Risk
Analytical Models
Company Purchases
Assurance
title_short Case study: an analytical model for the fraud detection In company purchases
title_full Case study: an analytical model for the fraud detection In company purchases
title_fullStr Case study: an analytical model for the fraud detection In company purchases
title_full_unstemmed Case study: an analytical model for the fraud detection In company purchases
title_sort Case study: an analytical model for the fraud detection In company purchases
author Reynaud, Raquel Alexandra Batista
author_facet Reynaud, Raquel Alexandra Batista
author_role author
dc.contributor.none.fl_str_mv Gonçalves, Rui Alexandre Henriques
RUN
dc.contributor.author.fl_str_mv Reynaud, Raquel Alexandra Batista
dc.subject.por.fl_str_mv Fraud Detection
Risk
Analytical Models
Company Purchases
Assurance
topic Fraud Detection
Risk
Analytical Models
Company Purchases
Assurance
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
publishDate 2020
dc.date.none.fl_str_mv 2020-08-31T16:57:24Z
2020-07-16
2020-07-16T00:00:00Z
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/10362/103106
TID:202512002
url http://hdl.handle.net/10362/103106
identifier_str_mv TID:202512002
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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