E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review
The e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems h...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-06-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2023.9020023 |
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| author | Abed Mutemi Fernando Bacao |
| author_facet | Abed Mutemi Fernando Bacao |
| author_sort | Abed Mutemi |
| collection | DOAJ |
| description | The e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry. |
| format | Article |
| id | doaj-art-03d47911c6a44617807f026c8d549975 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-03d47911c6a44617807f026c8d5499752025-02-02T22:18:05ZengTsinghua University PressBig Data Mining and Analytics2096-06542024-06-017241944410.26599/BDMA.2023.9020023E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature ReviewAbed Mutemi0Fernando Bacao1NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, PortugalNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, Lisboa 1070-312, PortugalThe e-commerce industry’s rapid growth, accelerated by the COVID-19 pandemic, has led to an alarming increase in digital fraud and associated losses. To establish a healthy e-commerce ecosystem, robust cyber security and anti-fraud measures are crucial. However, research on fraud detection systems has struggled to keep pace due to limited real-world datasets. Advances in artificial intelligence, Machine Learning (ML), and cloud computing have revitalized research and applications in this domain. While ML and data mining techniques are popular in fraud detection, specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce context. To bridge this gap, our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) methodology. We aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce landscape. Understanding the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated costs. Through our investigation, we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce fraud. Our paper examines the research on these techniques as published in the past decade. Employing the PRISMA approach, we conducted a content analysis of 101 publications, identifying research gaps, recent techniques, and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.https://www.sciopen.com/article/10.26599/BDMA.2023.9020023e-commercefraud detectionmachine learning (ml)systematic revieworganized retail fraud |
| spellingShingle | Abed Mutemi Fernando Bacao E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review Big Data Mining and Analytics e-commerce fraud detection machine learning (ml) systematic review organized retail fraud |
| title | E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review |
| title_full | E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review |
| title_fullStr | E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review |
| title_full_unstemmed | E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review |
| title_short | E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review |
| title_sort | e commerce fraud detection based on machine learning techniques systematic literature review |
| topic | e-commerce fraud detection machine learning (ml) systematic review organized retail fraud |
| url | https://www.sciopen.com/article/10.26599/BDMA.2023.9020023 |
| work_keys_str_mv | AT abedmutemi ecommercefrauddetectionbasedonmachinelearningtechniquessystematicliteraturereview AT fernandobacao ecommercefrauddetectionbasedonmachinelearningtechniquessystematicliteraturereview |