Predicting Account Receivables Outcomes with Machine-Learning

Detalhes bibliográficos
Autor(a) principal: Rebelo, Susana Lopes da Costa
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/134205
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Predicting Account Receivables Outcomes with Machine-LearningCash FlowAccount ReceivablesMachine-LearningLightGBMProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe Account Receivables (AR) of a company are considered an important determinant of a company’s Cash Flow – the backbone of a company’s financial performance or health. It has been proved that by efficiently managing the money owed by customers for goods and services (AR), a company can avoid financial difficulties and even stabilize results in moments of extreme volatility. The aim of this project is to use machine-learning and data visualization techniques to predict invoice outcomes and provide useful information and a solution using analytics to the collection management team. Specifically, this project demonstrates how supervised learning models can classify with high accuracy whether a newly created invoice will be paid earlier, on-time or later than the contracted due date. It is also studied how to predict the magnitude of the delayed payments by classifying them into interesting, delayed categories for the business: up to 1 month late, from 1 to 3 months late and delayed for more than 3 months. The developed models use real-life data from a multinational company in the manufacturing and automation industries and can predict payments with higher accuracy than the baseline achieved by the business.Henriques, Roberto André PereiraRUNRebelo, Susana Lopes da Costa2022-03-10T18:39:29Z2022-01-282022-01-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/134205TID:202960617enginfo: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:RCAAP2024-03-11T05:12:39Zoai:run.unl.pt:10362/134205Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:01.313071Repositório 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 Account Receivables Outcomes with Machine-Learning
title Predicting Account Receivables Outcomes with Machine-Learning
spellingShingle Predicting Account Receivables Outcomes with Machine-Learning
Rebelo, Susana Lopes da Costa
Cash Flow
Account Receivables
Machine-Learning
LightGBM
title_short Predicting Account Receivables Outcomes with Machine-Learning
title_full Predicting Account Receivables Outcomes with Machine-Learning
title_fullStr Predicting Account Receivables Outcomes with Machine-Learning
title_full_unstemmed Predicting Account Receivables Outcomes with Machine-Learning
title_sort Predicting Account Receivables Outcomes with Machine-Learning
author Rebelo, Susana Lopes da Costa
author_facet Rebelo, Susana Lopes da Costa
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Rebelo, Susana Lopes da Costa
dc.subject.por.fl_str_mv Cash Flow
Account Receivables
Machine-Learning
LightGBM
topic Cash Flow
Account Receivables
Machine-Learning
LightGBM
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2022
dc.date.none.fl_str_mv 2022-03-10T18:39:29Z
2022-01-28
2022-01-28T00:00:00Z
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/10362/134205
TID:202960617
url http://hdl.handle.net/10362/134205
identifier_str_mv TID:202960617
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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repository.mail.fl_str_mv
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