Utilizing a time-dependent graph convolutional neural network for fraudulent transaction identification
US11403643B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 24, 2020 |
| Grant date | Aug 2, 2022 |
| Priority date | — |
| Expiry date | Nov 13, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0601
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present disclosure relates to utilizing a graph convolutional neural network to generate similarity probabilities between pairs of digital identities associated with digital transactions based on time dependencies for use in identifying fraudulent transactions. For example, the disclosed systems can generate a transaction graph that includes nodes corresponding to digital identities. The disclosed systems can utilize a time-dependent graph convolutional neural network to generate node embeddings for the nodes based on the edge connections of the transaction graph. Further, the disclosed systems can utilize the node embeddings to determine whether a digital identity is associated with a fraudulent transaction.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.