Real-time detection of online new-account creation fraud using graph-based neural network modeling
US12255916B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 12, 2022 |
| Grant date | Mar 18, 2025 |
| Priority date | — |
| Expiry date | Apr 19, 2043 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L63/1483
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.