Detecting fraudulent personalities in networks of online auctioneers

Online auctions have gained immense popularity by creating an accessible environment for exchanging goods at reasonable prices. Not surprisingly, malevolent auction users try to abuse them by cheating others. In this paper we propose a novel method, 2-Level Fraud Spotting (2LFS), to model the techniques that fraudsters typically use to carry out fraudulent activities, and to detect fraudsters preemptively. Our key contributions are: (a) we mine user level features (e.g., number of transactions, average price of goods exchanged, etc.) to get an initial belief for spotting fraudsters, (b) we introduce network level features which capture the interactions between different users, and (c) we show how to combine both these features using a Belief Propagation algorithm over a Markov Random Field, and use it to detect suspicious patterns (e.g., unnaturally close-nit groups of people that trade mainly among themselves). Our algorithm scales linearly with the number of graph edges. Moreover, we illustrate the effectiveness of our algorithm on a real dataset collected from a large online auction site.