Extracting and reasoning about implicit behavioral evidences for detecting fraudulent online transactions in e-Commerce

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

8 Scopus Citations
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Author(s)

  • Jie Zhao
  • Wenping Zhang
  • Kaihang Zhang
  • Xu Chen
  • Deyu Tang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)109-121
Journal / PublicationDecision Support Systems
Volume86
Online published22 Apr 2016
Publication statusPublished - Jun 2016

Abstract

With the explosive growth of e-Commerce worldwide, there are also growing concerns about collusive fraudulent transaction attacks in e-Commerce. The main contribution of our research work is the design of a novel detection framework that can reason about implicit online user behavior for detecting collusive fraudulent transactions. Based on real transactional and user behavioral data collected from one of the largest e-Commerce platforms in the world, our experimental results confirm that the proposed detection framework can achieve an average true positive detection rate of 83% while the false alarm rate is kept at as low as 2.4%. To the best of our knowledge, this is one of the largest scale studies toward the detection of fraudulent transactions in e-Commerce. The managerial implication of our study is that administrators of e-Commerce platforms can apply our framework to detect and prevent fraudulent transaction attacks, and hence fair electronic trading is upheld in the ever expanding e-Commerce world.

Research Area(s)

  • Electronic Commerce, Evidence fusion, Fraud detection, Theory of evidence

Citation Format(s)