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UTILIZING A TIME-DEPENDENT GRAPH CONVOLUTIONAL NEURAL NETWORK FOR FRAUDULENT TRANSACTION IDENTIFICATION
UTILIZING A TIME-DEPENDENT GRAPH CONVOLUTIONAL NEURAL NETWORK FOR FRAUDULENT TRANSACTION IDENTIFICATION
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机译:利用时间依赖的图形卷积神经网络进行欺诈事务识别
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摘要
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.
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