Fraud Detection by Human Agents: A Pilot Study
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
A novel two-stage phased modeling framework for early fraud detection in online auctions
Expert Systems with Applications: An International Journal
Reputation inflation detection in a Chinese C2C market
Electronic Commerce Research and Applications
Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches
International Journal of Electronic Commerce
Survey: Combating online in-auction fraud: Clues, techniques and challenges
Computer Science Review
An effective early fraud detection method for online auctions
Electronic Commerce Research and Applications
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Fraud detection has become a common concern of the online auction websites. Fraudsters often manipulate reputation systems and commit non- delivery fraud. To deal with fraud in group behavior we consider network level features, such as users' beliefs of other users. In this paper we use the loopy belief propagation algorithm and apply it to network level fraud detection, classifying fraudsters, accomplices, as well as honest users. Our method shows good classification accuracy using real data.