Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning

Detalhes bibliográficos
Autor(a) principal: Crisóstomo, Laura Inês Bleeker Casquinha
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/10362/136684
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
id RCAP_ad5847290e07104fc1bceee3e9ae050d
oai_identifier_str oai:run.unl.pt:10362/136684
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine LearningData ScienceInternal AuditData AnalyticsMachine LearningSupervised LearningBinary ClassificationImbalanced LearningInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis report presents the work developed during the academic internship required for obtaining the Master’s Degree in Data Science and Advanced Analytics. The internship took place in the area of Data & Analytics of the Department for Internal Audit of Caixa Geral de Depósitos (Portugal), from the 14th of September 2020 to the 13th of June 2021. The internship’s goal was the introduction of machine learning to the Department of Internal Audit. In particular, the implementation of three machine learning pipelines to aid in audit activities of the institution, which systematically analyze operations that stand out from the implemented alarm system. The alarm system triggers alerts when an event disobeys a predefined methodology. Each triggering event is reviewed and processed individually by the auditors, either by being classified as a confirmed error or as a false positive. Confirmed errors frequently lead to recommendations to rectify the operations, while false positives are closed without a recommendation. The alerts’ triggers are defined by sets of arguably general and manually implemented rules, resulting in high trigger frequencies and low precisions. Trigger frequency, precision, and cost of miss rate differ for each alert. Based on the alerts’ trigger history data, three types of alerts were selected for improvements. The deployment of machine learning pipelines with classification models optimized the triggers' specificity while maintaining high sensitivity, which reduced the number of daily events that have to be reviewed by the auditors. This optimization maximizes the efficiency and productivity of the general alarm system and decreases the auditors’ workload.Pinheiro, Flávio Luís PortasRUNCrisóstomo, Laura Inês Bleeker Casquinha2022-04-20T10:21:47Z2022-04-112022-04-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/136684TID:202993833enginfo: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:RCAAP2024-03-11T05:14:38Zoai:run.unl.pt:10362/136684Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:44.898894Repositório 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 Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
title Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
spellingShingle Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
Crisóstomo, Laura Inês Bleeker Casquinha
Data Science
Internal Audit
Data Analytics
Machine Learning
Supervised Learning
Binary Classification
Imbalanced Learning
title_short Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
title_full Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
title_fullStr Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
title_full_unstemmed Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
title_sort Data Science for Internal Audit in Banking: Refinement of an Internal Audit Alarmistic System with Machine Learning
author Crisóstomo, Laura Inês Bleeker Casquinha
author_facet Crisóstomo, Laura Inês Bleeker Casquinha
author_role author
dc.contributor.none.fl_str_mv Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Crisóstomo, Laura Inês Bleeker Casquinha
dc.subject.por.fl_str_mv Data Science
Internal Audit
Data Analytics
Machine Learning
Supervised Learning
Binary Classification
Imbalanced Learning
topic Data Science
Internal Audit
Data Analytics
Machine Learning
Supervised Learning
Binary Classification
Imbalanced Learning
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2022
dc.date.none.fl_str_mv 2022-04-20T10:21:47Z
2022-04-11
2022-04-11T00:00:00Z
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/10362/136684
TID:202993833
url http://hdl.handle.net/10362/136684
identifier_str_mv TID:202993833
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
_version_ 1799138088351432704

Registros relacionados