Modern financial frauds are frequently automated through specialized malware that hijacks money transfers from the victim's computer. An insidious type of fraud consists in repeatedly stealing small amounts of funds over time. A reliable detection of these fraud schemes requires an accurate modeling of the user's spending pattern over time. In this paper, we propose PraudBuster, a framework that exploits the end user's recurrent vs. non-recurrent spending pattern to detect these sophisticated frauds. FraudBuster is based on a learning stage that builds, for each user, temporal profiles and quantifies the deviation of each incoming transaction from the learned model. The final output is the aggregated score that quantifies the risk of a user of being defrauded. In this setting, FraudBuster detects frauds as transactions that are not simply "anomalous", but that would change the user's spending profile. We deployed FraudBuster in the real-world setting of a national banking group and measured the detection performance, showing that it can outperform existing solutions.
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