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Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques

2020-03-03Book part Open access
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datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorConstante-Nicolalde, Fabián-Vinicio
dc.contributor.authorGuerra-Terán, Paulo
dc.contributor.authorPérez-Medina, Jorge-Luis
dc.date.accessioned2025-10-09T16:24:45Z
dc.date.available2025-10-09T16:24:45Z
dc.date.issued2020-03-03
dc.descriptionEISBN - 9783030425203
dc.descriptionSite: https://redi.cedia.edu.ec/document/251353
dc.description.abstractIn the domain of Big Data, the company’s supply chain has a very high-risk exposure and this must be observed from a preventive perspective, that is, act before such situations occur. As a company grows and diversifies the number of suppliers, customers and therefore increases its number of daily transactions and associated risks. Despite the innovation and improvements that have been incorporated into financial management, credit and debit cards are the main means of exchanging cash online, with the expansion of e-commerce, online shopping has also increased number of extortion cases that have been identified and that continues to expand greatly. It takes a lot of time, effort and investment to restore the impact of these damages. In this paper, we work with machine learning techniques, used in predicting smart supply chain fraud, are valuable for estimating, classifying whether a transaction is normal or fraudulent, and mitigating future dangers.eng
dc.description.sponsorshipThis work was made possible thanks to the financial support of “Universidad de Las Américas” from Ecuador and thanks to the participation of Polytechnic Institute of Leiria from Portugal.
dc.identifier.citationConstante-Nicolalde, FV., Guerra-Terán, P., Pérez-Medina, JL. (2020). Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_12.
dc.identifier.doi10.1007/978-3-030-42520-3_12
dc.identifier.eissn1865-0937
dc.identifier.isbn9783030425197
dc.identifier.isbn9783030425203
dc.identifier.issn1865-0929
dc.identifier.urihttp://hdl.handle.net/10400.8/14235
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-030-42520-3_12
dc.relation.ispartofCommunications in Computer and Information Science
dc.relation.ispartofApplied Technologies
dc.rights.uriN/A
dc.subjectFraud prediction
dc.subjectClassification approaches
dc.subjectBig Data Analysis
dc.titleFraud Prediction in Smart Supply Chains Using Machine Learning Techniqueseng
dc.typebook part
dspace.entity.typePublication
oaire.citation.titleCommunications in Computer and Information Science
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameConstante
person.givenNameFabián
person.identifier.orcid0000-0003-1747-6294
relation.isAuthorOfPublicationc96c2df4-2b4b-4b6a-a291-6242823cec30
relation.isAuthorOfPublication.latestForDiscoveryc96c2df4-2b4b-4b6a-a291-6242823cec30

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In the domain of Big Data, the company’s supply chain has a very high-risk exposure and this must be observed from a preventive perspective, that is, act before such situations occur. As a company grows and diversifies the number of suppliers, customers and therefore increases its number of daily transactions and associated risks. Despite the innovation and improvements that have been incorporated into financial management, credit and debit cards are the main means of exchanging cash online, with the expansion of e-commerce, online shopping has also increased number of extortion cases that have been identified and that continues to expand greatly. It takes a lot of time, effort and investment to restore the impact of these damages. In this paper, we work with machine learning techniques, used in predicting smart supply chain fraud, are valuable for estimating, classifying whether a transaction is normal or fraudulent, and mitigating future dangers.
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