Real-time detection of online new-account creation fraud using graph-based neural network modeling

Abstract

A method executes upon receiving data (email, IP address) associated with an account registration. In response, an encoding is applied to the data to generate a node vector. The node vector indexes a database of such node vectors that the system maintains (from prior registrations). The database potentially includes one or more node vector(s) that may have a given similarity to the encoded node vector. To determine whether there are such vectors present, a set of k-nearest neighbors to the encoded node vector are then obtained from the database. This set of k-nearest neighbors together with the encoded node vector comprise a virtual graph that is then fed as a graph input to a Graph Neural Network previously trained on a set of training data. The GNN generates a probability that the virtual graph represents a NAF. If the probability exceeds a configurable threshold, the system outputs an indication that the registration is potentially fraudulent, and a mitigation action is taken.