A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
This chapters discusses fraud detection specifically within the shipping industryShipping industry using data analyticsData analytics techniques. The shipping industry is experiencing significant growth due to globalization and the rise of e-commerce, particularly during the recent pandemic. This ex...
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Springer Science and Business Media Deutschland GmbH
2025
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| Summary: | This chapters discusses fraud detection specifically within the shipping industryShipping industry using data analyticsData analytics techniques. The shipping industry is experiencing significant growth due to globalization and the rise of e-commerce, particularly during the recent pandemic. This expansion attracts fraudsters who exploit the system by transporting illegal or banned items using fake declaration documents. The immense volume of shipments makes manual inspection and verification unsustainable, increasing operational costs and causing delays that affect the supply chain and raise consumer prices. An automated solutionAutomated solution is needed to address this issue and prevent further impacts on the industry and society. A study reviewed existing fraud detectionFraud detection models and identified the most effective algorithm for the shipping industry. Using RapidMiner, various algorithms were tested. The study found that k-NNK-NN is the most effective in terms of performance and accuracy for detecting fraud within the shipping industry. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
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