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

Abstract: a method executes upon receiving data associated with a registration. in response, an encoding is applied to the data to generate a vector. the vector indexes a database of such 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. if the probability exceeds a configurable threshold, the system outputs an indication that the registration is potentially fraudulent, and a mitigation action is taken.

Inventor(s): Nadav George Costa, Ziv Eli

CPC Classification: H04L63/1441 ({Countermeasures against malicious traffic (countermeasures against attacks on cryptographic mechanisms )})

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