Pricing fraud detection in online shopping malls using a finite mixture model

Kwanho Kim, Yerim Choi, Jonghun Park

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Although pricing fraud is an important issue for improving service quality of online shopping malls, research on automatic fraud detection has been limited. In this paper, we propose an unsupervised learning method based on a finite mixture model to identify pricing frauds. We consider two states, normal and fraud, for each item according to whether an item description is relevant to its price by utilizing the known number of item clusters. Two states of an observed item are modeled as hidden variables, and the proposed models estimate the state by using an expectation maximization (EM) algorithm. Subsequently, we suggest a special case of the proposed model, which is applicable when the number of item clusters is unknown. The experiment results show that the proposed models are more effective in identifying pricing frauds than the existing outlier detection methods. Furthermore, it is presented that utilizing the number of clusters is helpful in facilitating the improvement of pricing fraud detection performances.

Original languageEnglish
Pages (from-to)195-207
Number of pages13
JournalElectronic Commerce Research and Applications
Volume12
Issue number3
DOIs
StatePublished - May 2013

Keywords

  • e-Commerce
  • Expectation maximization algorithm
  • Finite mixture model
  • Fraud detection
  • Online shopping
  • Pricing fraud

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