Patent ID: 11900382
Assignee: MASTERCARD INTERNATIONAL INCORPORATED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A method for detecting fraudulent transactions, the method comprising:
receiving, by a server, first transaction data for a first transaction;
providing, by the server, the first transaction data as input to a neural network that is trained based on a set of static features and a plurality of dynamic features, wherein the set of static features is determined from historical transaction data of a plurality of historical transactions and the plurality of dynamic features are determined from a graph that includes a plurality of nodes and a plurality of weighted edges between the plurality of nodes, and wherein the plurality of nodes include a set of merchant nodes corresponding to a set of merchants in the plurality of historical transactions and a set of consumer nodes corresponding to a set of consumers in the plurality of historical transactions;
detecting, by the server, the first transaction as one of a fraudulent transaction and a legitimate transaction based on an output of the trained neural network for the first transaction data;
receiving, by the server, real-time transactions data for one or more new transactions;
determining, by the server, that a change in the plurality of dynamic features caused by the one or more real-time transactions data is greater than or equal to a threshold level;
responsive to the determination that the change in the plurality of dynamic features is greater than or equal to the threshold level:
updating, by the server, the plurality of dynamic features based on the determination that the change is greater than or equal to the threshold level to limit use of computing resources for the updating only when the change is greater than or equal to the threshold level;
updating, by the server, the graph based on the updated plurality of dynamic features; and

retraining, by the server, the neural network based on the updated graph.