Publication: Fraud Detection in Shipping Industry using K-NN Algorithm
Date
2021
Authors
Subramaniam G.
Mahmoud M.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Science and Information Organization
Abstract
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
Description
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