Systems and methods for training and applying machine learning systems in fraud detection

A computer-implemented method for identifying unauthorized activity using a machine learning model, the method being performed by at least one processor and comprising:

• determining, using a machine learning model, an initial unauthorized activity score for a processed action occurring on a transaction channel computing device;
• receiving streaming data from at least one streaming data source and batch data from at least one batch data source, wherein the streaming data and the batch data correspond to the processed action occurring on the transaction channel computing device;
• processing the streaming data to generate at least one engineered streaming feature corresponding to the processed action;
• processing the batch data to generate at least one engineered batch feature corresponding to the processed action;
• generating an integrated feature based on the at least one engineered streaming feature and the at least one engineered batch feature;
• calculating, using the machine learning model, an updated unauthorized activity score for the processed action based on the integrated feature;
• and
• transmitting a notification, via a network, to a user interface device when the updated unauthorized activity score differs from the initial unauthorized activity score by a predetermined enrichment threshold.

A non-transitory computer-readable medium storing a set of instructions for identifying unauthorized activity using a machine learning model, the set of instructions comprising:

• one or more instructions that, when executed by at least one processor of a computing system, cause the computing system to:
• determine, using a machine learning model, an initial unauthorized activity score for a processed action occurring on a transaction channel computing device;
• receive streaming data from at least one streaming data source and batch data from at least one batch data source, wherein the streaming data and the batch data correspond to the processed action occurring on the transaction channel computing device;
• process the streaming data to generate at least one engineered streaming feature corresponding to the processed action;
• processing the batch data to generate at least one engineered batch feature corresponding to the processed action;
• generate an integrated feature based on the at least one engineered streaming feature and the at least one engineered batch feature;
• calculate, using the machine learning model, an updated unauthorized activity score for the processed action based on the integrated feature;
• and
• transmit a notification, via a network, to a user interface device when the updated unauthorized activity score differs from the initial unauthorized activity score by a predetermined enrichment threshold.