Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction

Detalhes bibliográficos
Autor(a) principal: Costa, João Filipe Dias Gomes de Morais
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/10362/164589
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality ReductionHealth InsuranceFraud DetectionUnsupervised LearningAnomaly DetectionAutoencoderSHAPSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureSDG 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 Data ScienceIn the insurance sector, machine learning techniques are widely employed to aid auditing teams in identifying potentially fraudulent claims. At Future Healthcare Group, an unsupervised anomaly detection (UAD) model has been deployed to support a dedicated team in the audit process. This model incorporates an autoencoder for dimensionality reduction of part of its feature space. This project starts with the question: 'Is it possible to increase the efficiency of the current UAD model by increasing its interpretability with SHapley Addictive Explanations (SHAP)?'. Due to its 'nested architecture' the direct implementation of SHAP explanations directly into this model poses computational challenges namely in uncovering the information compressed by the autoencoder. This project aimed at developing a framework that efficiently integrates SHAP explanations into the unsupervised anomaly detection model. This project is divided in two steps: In the first step, it focuses on building the framework; in the second, the framework output is evaluated. The framework increased the efficiency of the model. This was achieved mainly by indirectly increasing the UAD model performance. The presence of the explanations allowed to uncover observations classified as anomalous due to its rarity that were not true anomalies by business definition. This allowed the pre-filtration of these, which contributed indirectly to the increased performance of the based model. In summary, the developed framework offers an efficient solution for integrating SHAP explanations into an unsupervised anomaly detection model, particularly when a part of the feature space undergoes compression via an autoencoder.Henriques, Roberto André PereiraRUNCosta, João Filipe Dias Gomes de Morais2024-03-07T18:48:10Z2024-02-022024-02-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/164589TID:203544439enginfo: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:RCAAP2024-03-11T05:53:20Zoai:run.unl.pt:10362/164589Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T04:00:16.368293Repositório 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 Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
title Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
spellingShingle Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
Costa, João Filipe Dias Gomes de Morais
Health Insurance
Fraud Detection
Unsupervised Learning
Anomaly Detection
Autoencoder
SHAP
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
title_full Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
title_fullStr Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
title_full_unstemmed Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
title_sort Enhancing Fraud Detection in the Insurance Industry: Integrating SHAP explanations into an Unsupervised Anomaly Detection Model with Autoencoder-based Dimensionality Reduction
author Costa, João Filipe Dias Gomes de Morais
author_facet Costa, João Filipe Dias Gomes de Morais
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Costa, João Filipe Dias Gomes de Morais
dc.subject.por.fl_str_mv Health Insurance
Fraud Detection
Unsupervised Learning
Anomaly Detection
Autoencoder
SHAP
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
SDG 16 - Peace, justice and strong institutions
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Health Insurance
Fraud Detection
Unsupervised Learning
Anomaly Detection
Autoencoder
SHAP
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
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 Data Science
publishDate 2024
dc.date.none.fl_str_mv 2024-03-07T18:48:10Z
2024-02-02
2024-02-02T00:00:00Z
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/10362/164589
TID:203544439
url http://hdl.handle.net/10362/164589
identifier_str_mv TID:203544439
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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