System and methods for iterative synthetic data generation and refinement of machine learning models

A computer-implemented method for iterative synthetic data generation, the computer-implemented method comprising:

• receiving input data for analysis and expansion;
• analyzing the input data using a machine learning model to identify an emerging pattern in the input data;
• extracting common data characteristics from the identified emerging pattern in order to determine a data scenario;
• utilizing encoding and decoding logic to expand the scope of the data scenario;
• based on the expanded scope of the data scenario, generating multiple synthetic data sets using a generative adversarial neural network;
• training one or more additional machine learning models using varied combinations of the multiple synthetic data sets and real input data;
• and
• generating a machine learning model ensemble using a subset of the one or more additional machine learning models in order to identify additional emerging patterns in the received input data.

A computer program product for iterative synthetic data generation, the computer program product comprising at least one non-transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising:

• an executable portion configured for receiving input data for analysis and expansion;
• an executable portion configured for analyzing the input data using a machine learning model to identify an emerging pattern in the input data;
• an executable portion configured for extracting common data characteristics from the identified emerging pattern in order to determine a data scenario;
• an executable portion configured for utilizing encoding and decoding logic to expand scope of the data scenario;
• an executable portion configured for, based on the expanded scope of the data scenario, generating multiple synthetic data sets using a generative adversarial neural network;
• an executable portion configured for training one or more additional machine learning models using varied combinations of the multiple synthetic data sets and real input data;
• and
• an executable portion configured for generating a machine learning model ensemble using a subset of the one or more additional machine learning models in order to identify additional emerging patterns in the received input data.