Fraud Detection with Entity Resolution and Graph Neural Networks

Online fraud is an ever-growing issue for finance, e-commerce and other related industries. In response to this threat, organizations use fraud detection mechanisms based on machine learning and behavioral analytics. These technologies enable the detection of unusual patterns, abnormal behaviors, and fraudulent activities in real time. Unfortunately, often only the current transaction, e.g. an order, is taken into consideration, or the process is based solely on historic data from the customer’s profile, which is identified by a customer id. However, professional fraudsters may create customer profiles using low value transactions to build up a positive image of their profile. Additionally, they might create multiple similar profiles at the same time. It is only after the fraud took place that the attacked company realizes that these customer profiles were related to each other. Using entity resolution it is possible to easily combine different customer profiles into a single 360° customer view, allowing one to see the full picture of all historic transactions. While using this data in machine learning, e.g. using a neural network or even a simple linear regression, would already provide additional value for the resulting model, the real value arises from also looking at how the individual transactions are connected to each other. This is where graph neural networks (GNN) come into play. Beside looking at features extracted from the transactional records, they offer also the possibility to look at features generated from the graph edges (how transactions are linked with each other) or even just the general layout of the entity graph. <a href="https://towardsdatascience.com/fraud-detection-with-entity-resolution-and-graph-neural-networks-c70f43b9e1f2">Read More</a>