Citation

Hewiagh, Ali and Ramakrishnan, Kannan and Yap, Timothy Tzen Vun and Tan, Ching Seong (2021) Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse. Recycling, 6 (4). p. 65. ISSN 2313-4321

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Abstract

Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.

Item Type:	Article
Uncontrolled Keywords:	Refuse and refuse disposal, Waste management, recycling, machine learning
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD2350.8-2356 Large industry. Factory system. Big business > HD3611-4730.9 Industrial policy. The state and industrial organization
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2021 06:25
Last Modified:	04 Nov 2021 06:25
URII:	http://shdl.mmu.edu.my/id/eprint/9757

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