A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2023 |
| Outros Autores: | |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/156397 |
Resumo: | Mutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5 |
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A numeric-based machine learning design for detecting organized retail fraud in digital marketplacesGeneralSDG 16 - Peace, Justice and Strong InstitutionsMutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNMutemi, AbedBacao, Fernando2023-08-07T22:17:39Z2023-08-022023-08-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article16application/pdfhttp://hdl.handle.net/10362/156397eng2045-2322PURE: 68264720https://doi.org/10.1038/s41598-023-38304-5info: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:RCAAP2024-03-11T05:39:02Zoai:run.unl.pt:10362/156397Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:26.236575Repositório 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 |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| title |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| spellingShingle |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces Mutemi, Abed General SDG 16 - Peace, Justice and Strong Institutions |
| title_short |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| title_full |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| title_fullStr |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| title_full_unstemmed |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| title_sort |
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces |
| author |
Mutemi, Abed |
| author_facet |
Mutemi, Abed Bacao, Fernando |
| author_role |
author |
| author2 |
Bacao, Fernando |
| author2_role |
author |
| dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
| dc.contributor.author.fl_str_mv |
Mutemi, Abed Bacao, Fernando |
| dc.subject.por.fl_str_mv |
General SDG 16 - Peace, Justice and Strong Institutions |
| topic |
General SDG 16 - Peace, Justice and Strong Institutions |
| description |
Mutemi, A., & Bacao, F. (2023). A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces. Scientific Reports, 13(1), 1-16. [12499]. https://doi.org/10.1038/s41598-023-38304-5 |
| publishDate |
2023 |
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2023-08-07T22:17:39Z 2023-08-02 2023-08-02T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
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http://hdl.handle.net/10362/156397 |
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http://hdl.handle.net/10362/156397 |
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eng |
| language |
eng |
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2045-2322 PURE: 68264720 https://doi.org/10.1038/s41598-023-38304-5 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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16 application/pdf |
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