Fraud Detection in Networks

Abstract

Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. The fraudulent behavior in money laundering may manifest itself through unusual patterns in financial transaction networks. Most commonly, these networks are represented as attributed graphs, with numerical features complementing relational information. We present a survey on anomaly detection techniques used for fraud detection that exploit both the graph structure underlying the data and the contextual information contained in the attributes. While the focus in on methods that are suited for financial frauds, we extend the review to other types of frauds on networks.