E-Commerce Fraud Detection Based on Machine Learning Techniques
Kommu Manikanta,
G.Rajamani,
R.Vinay,
P.Sowmya,
C.Sandeep,
N.Pavan Kumar
Sarcasm 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.
