Online Modeling of Proactive Moderation System for Auction Fraud Detection

Abstract

Weconsider the problem of buildingonline machine-learned models for detecting auction frauds in e-commence web sites. Since the emergence of the world wide web, online shop ping and online auction have gained more and more pop ularity. While people are enjoying the benefits from on line trading, criminals are also taking advantages to conduct fraudulent activities against honest parties to obtain illegal profit. Hence proactive fraud-detection moderation systems are commonly applied in practice to detect and prevent such illegal and fraud activities. Machine-learned models, espe cially those that are learned online, are able to catch frauds more efficiently and quickly than human-tuned rule-based systems. In this paper, we propose an online probit model framework which takes online feature selection, coefficient bounds from human knowledge and multiple instance learn ing into account simultaneously. By empirical experiments on a real-world online auction fraud detection data we show that this model can potentially detect more frauds and sig nificantly reduce customer complaints compared to several baseline models and the human-tuned rule-based system