Patent ID: 11907954
Assignee: PAYPAL, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G  H

Claim 0:
1. A method, comprising:
generating, by a computer system, a transaction network graph from an initial set of transactions with transaction attributes, wherein transactions in the initial set of transactions include known labels;
generating, by the computer system from a training set of transactions using a first graph embedding routine, a first transaction matrix, wherein the first graph embedding routine is based on anomalies in related transactions occurring at one or more nodes in the transaction network graph that are one or more hops away, and wherein the training set includes a first subset of transactions in the transaction network graph;
generating, by the computer system from the training set of transactions using a second, different graph embedding routine, a second, different transaction matrix, wherein the second, different graph embedding routine is based on anomalies in neighborhoods of similar transactions that have multiple shared attributes, and wherein generating the second, different matrix includes aggregating, for a given node assigned an anomaly label, attributes of transactions occurring at nodes within a neighborhood of nodes of the anomaly labeled node and attributes of transactions occurring at the anomaly labeled node;
generating, by the computer system, a final embedded matrix from the first transaction matrix and the second, different transaction matrix, wherein one or more attributes of transactions in the final embedded matrix differ from attributes of corresponding transactions in the training set of transactions;
training, by the computer system using the final embedded matrix and a testing set of transactions that includes a second, different subset of transactions in the transaction network graph, a machine learning model;
determining, by the computer system using the trained machine learning model, whether one or more unlabeled transactions are anomalous; and
in response to determining that at least one of the one or more unlabeled transactions are anomalous, rejecting the transaction.