eRiskCom: an e-commerce risky community detection platform

In e-commerce scenarios, frauds events such as telecom fraud, insurance fraud, and fraudulent transactions, bring a huge amount of loss to merchants or users. Identification of fraudsters helps regulators take measures for targeted control. Given a set of fraudsters and suspicious users observed from victims’ reports, how can we effectively distinguish risky users closely related to them from the others for further investigation by human experts? Fraudsters take camouflage actions to hide from being discovered; complex features on users are hard to deal with; patterns of fraudsters are sometimes difficult to explain by human knowledge; and real-world applications involve millions of users. All this makes the question hard to answer. To this end, we design eRiskCom, an e-commerce risky community detection platform to detect risky groups containing identified fraudsters and other closely related users. With the hypothesis that users who interact frequently with fraudsters are more likely to come from the same “risky community,” we construct a connected graph expanded from the identified fraudsters and suspicious users. Next, graph partition is employed to get knowledge of assignment of identified users to potential risky communities, followed by pruning to discover the core members of each community. Finally, top-K users with a high risk score in the neighborhood of core members of each potential community form a final risky community. The extensive experiments are conducted to analyze the effect of our platform components on the alignment with requirements of practical scenarios, and experimental results further demonstrate that eRiskCom is effective and easy to deploy for real-world applications.