Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds

Detalhes bibliográficos
Autor(a) principal: Silva, Sara Filipa Santos Pereira Fernandes da
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/163939
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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spelling Machine Learning in Audit of Operations: Predicting the Error Rate of European FundsOperational AuditsMonetary Unit SamplingMachine LearningSupervised Regression AlgorithmsSDG 16 - Peace, justice and strong institutionsDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIn the field of auditing of operations, to assess error rates for Book Values (BV) in a European funds population, Monetary Unit Sampling (MUS), a widely used sampling methodology, enables auditors to extrapolate about the population being materially misstated. The thesis delved into the potential of incorporating supervised Machine Learning (ML) regression models to innovate how error rates are identified in European funds, given the limited exploration of ML’s effectiveness in auditing. Likewise, the study focused on showcasing the capability of predicted error rates align closely with the true misstatement values operations, previously selected through MUS sampling method. A pure model-based conceptual framework approach was developed introducing supervised ML regression models, including tree-based algorithms, such as Decision Trees, eXtreme Gradient Boosting, Categorical Boosting and Random Forest Quantile, and the Multilayer Perceptron algorithm. Additionally, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics, data visualization tools and Repeated Stratified Cross-Validation were used for models’ assessment. Furthermore, data samples were provided by the IGF entity, from training, validation and testing purposes. Results exhibited the proficiency of ML algorithms in predicting specific pattern performances although generalization across error rates remained a challenge. This advancement sets the stage for moving beyond standard the standard MUS method towards Artificial Intelligence (AI) applications in audit of operations.Coelho, Pedro Miguel Pereira SimõesRUNSilva, Sara Filipa Santos Pereira Fernandes da2024-02-22T17:15:14Z2024-01-302024-01-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/163939TID:203526759enginfo: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:RCAAP2024-03-11T05:49:46Zoai:run.unl.pt:10362/163939Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:59:57.040013Repositório 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 Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
title Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
spellingShingle Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
Silva, Sara Filipa Santos Pereira Fernandes da
Operational Audits
Monetary Unit Sampling
Machine Learning
Supervised Regression Algorithms
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
title_full Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
title_fullStr Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
title_full_unstemmed Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
title_sort Machine Learning in Audit of Operations: Predicting the Error Rate of European Funds
author Silva, Sara Filipa Santos Pereira Fernandes da
author_facet Silva, Sara Filipa Santos Pereira Fernandes da
author_role author
dc.contributor.none.fl_str_mv Coelho, Pedro Miguel Pereira Simões
RUN
dc.contributor.author.fl_str_mv Silva, Sara Filipa Santos Pereira Fernandes da
dc.subject.por.fl_str_mv Operational Audits
Monetary Unit Sampling
Machine Learning
Supervised Regression Algorithms
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Operational Audits
Monetary Unit Sampling
Machine Learning
Supervised Regression Algorithms
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2024
dc.date.none.fl_str_mv 2024-02-22T17:15:14Z
2024-01-30
2024-01-30T00:00:00Z
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/10362/163939
TID:203526759
url http://hdl.handle.net/10362/163939
identifier_str_mv TID:203526759
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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