Fraud Detection in Shipping Industry using K-NN Algorithm
Nearest 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; Crime
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Science and Information Organization
2023
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my.uniten.dspace-265412023-05-29T17:11:45Z Fraud Detection in Shipping Industry using K-NN Algorithm Subramaniam G. Mahmoud M.A. 57223391179 55247787300 Nearest 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; Crime The 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. � 2021 Final 2023-05-29T09:11:45Z 2023-05-29T09:11:45Z 2021 Article 10.14569/IJACSA.2021.0120460 2-s2.0-85105785767 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105785767&doi=10.14569%2fIJACSA.2021.0120460&partnerID=40&md5=01552b219405b7b58feafb9f6cb93c42 https://irepository.uniten.edu.my/handle/123456789/26541 12 4 466 475 All Open Access, Gold Science and Information Organization Scopus |
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Nearest 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; Crime |
| author2 |
57223391179 |
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57223391179 Subramaniam G. Mahmoud M.A. |
| format |
Article |
| author |
Subramaniam G. Mahmoud M.A. |
| spellingShingle |
Subramaniam G. Mahmoud M.A. Fraud Detection in Shipping Industry using K-NN Algorithm |
| author_sort |
Subramaniam G. |
| title |
Fraud Detection in Shipping Industry using K-NN Algorithm |
| title_short |
Fraud Detection in Shipping Industry using K-NN Algorithm |
| title_full |
Fraud Detection in Shipping Industry using K-NN Algorithm |
| title_fullStr |
Fraud Detection in Shipping Industry using K-NN Algorithm |
| title_full_unstemmed |
Fraud Detection in Shipping Industry using K-NN Algorithm |
| title_sort |
fraud detection in shipping industry using k-nn algorithm |
| publisher |
Science and Information Organization |
| publishDate |
2023 |
| _version_ |
1806425830153256960 |
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13.214268 |