Systems and methods for formatting data using a recurrent neural network

A method for formatting data, the method comprising:

• receiving data comprising a plurality of data value sequences;
• generating conditional probabilities using a machine learning model based on the plurality of data value sequences, wherein the machine learning model is configured to output conditional probabilities of subsequent data values based on preceding data values in the received data;
• determining a data format of at least a subset of the plurality of data value sequences based on the generated conditional probabilities;
• generating reformatted data based on the determined data format;
• and
• training a synthetic data model to generate synthetic data based on the reformatted data after generating the reformatted data based on the determined data format.

A non-transitory computer-readable medium including instructions that are executable by one or more processors to perform operations comprising:

• receiving data comprising a plurality of data value sequences;
• generating conditional probabilities using a machine learning model based on the plurality of data value sequences, wherein the machine learning model is configured to output conditional probabilities of subsequent data values based on preceding data values in the received data;
• generating reformatted data based on the received data by reformatting at least one of the plurality of data value sequences according to a standard format;
• and
• training a synthetic data model to generate synthetic data based on the reformatted data after generating the reformatted data based on the determined data format.