TY - JOUR
T1 - E-Commerce Fraud Detection Based on Machine Learning Techniques
T2 - Systematic Literature Review
AU - Mutemi, Abed
AU - Bação, Fernando
N1 - Mutemi, A., & Bação, F. (2024). E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review. Big Data Mining and Analytics, 7(2), 419-444. https://doi.org/10.26599/BDMA.2023.9020023
PY - 2024/6
Y1 - 2024/6
N2 - 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, and cloud computing have revitalized research and applications in this domain. While machine learning and data mining techniques are popular in fraud detection, specific reviews focusing on their application in ecommerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of machine learning 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 machine learning 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.
AB - 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, and cloud computing have revitalized research and applications in this domain. While machine learning and data mining techniques are popular in fraud detection, specific reviews focusing on their application in ecommerce platforms like eBay and Facebook are lacking depth. Existing reviews provide broad overviews but fail to grasp the intricacies of machine learning 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 machine learning 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.
KW - E-commerce
KW - Fraud detection
KW - machine learning
KW - systematic review
KW - organized retail fraud
UR - http://www.scopus.com/inward/record.url?scp=85192069621&partnerID=8YFLogxK
UR - https://www.webofscience.com/wos/woscc/full-record/WOS:001252837000014
U2 - 10.26599/BDMA.2023.9020023
DO - 10.26599/BDMA.2023.9020023
M3 - Review article
SN - 2096-0654
VL - 7
SP - 419
EP - 444
JO - Big Data Mining and Analytics
JF - Big Data Mining and Analytics
IS - 2
ER -