E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review

Research output: Contribution to journalReview articlepeer-review

11 Citations (Scopus)
696 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)419-444
Number of pages26
JournalBig Data Mining and Analytics
Volume7
Issue number2
Early online date16 Oct 2023
DOIs
Publication statusPublished - Jun 2024

Keywords

  • E-commerce
  • Fraud detection
  • machine learning
  • systematic review
  • organized retail fraud

Fingerprint

Dive into the research topics of 'E-Commerce Fraud Detection Based on Machine Learning Techniques: Systematic Literature Review'. Together they form a unique fingerprint.

Cite this