Feature Extraction for Fraud Detection in Electronic Marketplaces

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Electronic markets are software systems that enable online transactions between buyers and sellers. One of the major challenges in these markets is to establish the notion of trust among users. This is normally addressed by introducing a reputation system that allows users to be evaluated for each transaction they perform. This work considers the problem of detecting fraudulent behavior of users against reputation systems in Electronic Marketplaces. We select and exhibit seventeen features with good discrimination power that are effective for this task, and we conducted experiments using data from a real-world dataset from a large Brazilian marketplace, including a list of known fraudsters identified by fraud experts. As a quick and first application of these features, we find out how a minimal number of features k could be used as a stronger evidence of fraud. With k = 1 we cover as much as 97% of known frauds, but the precision is only 14.31% (F-measure 0.25). The best F-measure is 0.43 and occurs for k = 4 and k = 5. Since many sellers who fraud the reputation system are still undetected, the computed precisions are not reliable. Almost all supposed false positives with at least ten features were manually checked and confirmed by experts to have fraudulent behavior, changing precision from 47% to at least 98%, for k = 10. At the end, the fraudster list was increased by 32% by this first analysis and the largest reviewed F-measure is 0.60.