Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-10-01
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| Series: | Recycling |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-4321/6/4/65 |
| _version_ | 1797501122180546560 |
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| author | Ali Hewiagh Kannan Ramakrishnan Timothy Tzen Vun Yap Ching Seong Tan |
| author_facet | Ali Hewiagh Kannan Ramakrishnan Timothy Tzen Vun Yap Ching Seong Tan |
| author_sort | Ali Hewiagh |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T03:13:48Z |
| format | Article |
| id | doaj.art-9ad180bc031d40e5aab1ec74e8cfd9d9 |
| institution | Directory Open Access Journal |
| issn | 2313-4321 |
| language | English |
| last_indexed | 2024-03-10T03:13:48Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Recycling |
| spelling | doaj.art-9ad180bc031d40e5aab1ec74e8cfd9d92023-11-23T10:21:23ZengMDPI AGRecycling2313-43212021-10-01646510.3390/recycling6040065Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption AbuseAli Hewiagh0Kannan Ramakrishnan1Timothy Tzen Vun Yap2Ching Seong Tan3Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, MalaysiaFaculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, MalaysiaiCYCLE, Shangyu District, Shaoxing 312000, ChinaOnline 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%.https://www.mdpi.com/2313-4321/6/4/65waste managementrecyclingmachine learningonline fraudsfraud detection |
| spellingShingle | Ali Hewiagh Kannan Ramakrishnan Timothy Tzen Vun Yap Ching Seong Tan Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse Recycling waste management recycling machine learning online frauds fraud detection |
| title | Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse |
| title_full | Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse |
| title_fullStr | Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse |
| title_full_unstemmed | Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse |
| title_short | Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse |
| title_sort | waste management system fraud detection using machine learning algorithms to minimize penalties avoidance and redemption abuse |
| topic | waste management recycling machine learning online frauds fraud detection |
| url | https://www.mdpi.com/2313-4321/6/4/65 |
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