Quick survey of graph-based fraud detection methods
This is an incremental survey that compiles existing methods for fraud detection in graph-based data, targeting researchers and practitioners in fields like finance and cybersecurity.
The paper surveys anomaly detection techniques for fraud detection that utilize both graph structure and contextual attributes, addressing the problem of identifying fraudulent patterns in relational data like financial transactions and social media.
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. Financial transactions, customer reviews, social media posts are all characterized by relational information. In these networks, fraudulent behaviour may appear as a distinctive graph edge, such as spam message, a node or a larger subgraph structure, such as when a group of clients engage in money laundering schemes. 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.