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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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Main Authors: Subramaniam G.A.L., Mahmoud M.A., Abdulwahid S.N., Gunasekaran S.S.
Other Authors: 57223391179
Format: Book chapter
Published: Springer Science and Business Media Deutschland GmbH 2025
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id my.uniten.dspace-36957
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spelling my.uniten.dspace-369572025-03-03T15:46:06Z A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques Subramaniam G.A.L. Mahmoud M.A. Abdulwahid S.N. Gunasekaran S.S. 57223391179 55247787300 57361650900 55652730500 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. Final 2025-03-03T07:46:06Z 2025-03-03T07:46:06Z 2024 Book chapter 10.1007/978-3-031-67317-7_2 2-s2.0-85205000211 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85205000211&doi=10.1007%2f978-3-031-67317-7_2&partnerID=40&md5=6a7eaf0a49c03181a22ef8641b068b39 https://irepository.uniten.edu.my/handle/123456789/36957 553 15 26 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description 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.
author2 57223391179
author_facet 57223391179
Subramaniam G.A.L.
Mahmoud M.A.
Abdulwahid S.N.
Gunasekaran S.S.
format Book chapter
author Subramaniam G.A.L.
Mahmoud M.A.
Abdulwahid S.N.
Gunasekaran S.S.
spellingShingle Subramaniam G.A.L.
Mahmoud M.A.
Abdulwahid S.N.
Gunasekaran S.S.
A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
author_sort Subramaniam G.A.L.
title A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
title_short A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
title_full A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
title_fullStr A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
title_full_unstemmed A Location-Based Fraud Detection in Shipping Industry Using Machine Learning Comparison Techniques
title_sort location-based fraud detection in shipping industry using machine learning comparison techniques
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2025
_version_ 1825816250243088384
score 13.244413

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