Publication:
Fraud Detection in Shipping Industry using K-NN Algorithm

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dc.citedby1
dc.contributor.authorSubramaniam G.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorid57223391179en_US
dc.contributor.authorid55247787300en_US
dc.date.accessioned2023-05-29T09:11:45Z
dc.date.available2023-05-29T09:11:45Z
dc.date.issued2021
dc.descriptionNearest neighbor search; Pattern recognition; Ships; Data driven; E- commerces; Fraud detection; Global trade; k-NN algorithm; Shipping companies; Shipping industry; Technological innovation; Trade liberalizations; Volume growth; Crimeen_US
dc.description.abstractThe shipment industry is going through tremendous growth in volume thanks to technological innovation in e-commerce and global trade liberalization. Volume growth also means a rise in fraud cases involving smuggling and false declaration of shipments. Shipping companies and customs are mostly relying on routine random inspection thus finding fraud is often by chance. As the volume increases dramatically it would no longer be sustainable and effective for both shipment companies and customs to pursue traditional fraud detection strategies. Other related papers on this area have proven that intelligent data-driven fraud detection is proven to be far more effective than routine inspections. However, the challenge in data-driven detection is its effectiveness are often reliant on the availability of data and the various fraud mechanism used by fraudsters to commit shipment related fraud. As such in this paper, we review and subsequently identify the most optimized approaches and algorithms to detect fraud effectively within the shipping industry. We also identify factors that influence fraud activity, review existing fraud detection models, develop the detection framework and implement the framework using the Rapidminer tool. � 2021en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14569/IJACSA.2021.0120460
dc.identifier.epage475
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85105785767
dc.identifier.spage466
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85105785767&doi=10.14569%2fIJACSA.2021.0120460&partnerID=40&md5=01552b219405b7b58feafb9f6cb93c42
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26541
dc.identifier.volume12
dc.publisherScience and Information Organizationen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Computer Science and Applications
dc.titleFraud Detection in Shipping Industry using K-NN Algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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