Recurrent neural network identifying anomalies in instructions data

A computing system using a recurrent neural network to identify anomalies in instructions data, the computing system comprising:

• a processor;
• and
• a memory device operatively coupled with the processor, the memory device storing a data validation application having executable code that, when executed, causes the processor to perform steps of:
• store in the memory device a first risk level, a second risk level and a third risk level;
• receive and store transfer instructions data, the transfer instructions data including information to enable an electronic data transfer from a source to a destination;
• train a recurrent neural network to build a machine learning model using previously stored data related to the source;
• evaluate the transfer instructions data using the model to identify any anomality representing one of the first through third risk levels associated with the transfer instructions data;
• process the electronic data transfer when the first risk level is identified by the model;
• process the electronic data transfer when the second risk level is identified by the model and the stored data includes preauthorization by the source;
• and
• generate an alert message when the third risk level is identified by the model.