Abstract: A computer-implemented system and method for generating and implementing real-time optimized savings recommendations during online purchase checkout processes. The recommendations may be in the form of personalized digital nudges designed to influence the user in a manner that furthers a savings goal.

Abstract: A method including retrieving first data from first and second databases. The databases include different, incompatible formats and described different aspect of subjects. The data in the respective databases is referenceable using a common key type. The method also includes converting, into combined data, the first data and the second data into a canonical form configured for use as input to a machine learning model. The method also includes generating, using the common key type, pre-processed data by correlating, for ones of the subjects in the combined data, the first aspect of the subjects to the second aspect of the subjects. The machine learning model takes, as an input, the first aspect and the second aspect for each of the subjects in the pre-processed data, and generates, as an output, a prediction for a selected subject in the subjects. The method also includes presenting the output.

Abstract: A computer-implemented system and method for generating and implementing real-time optimized savings recommendations during online purchase checkout processes. The recommendations may be in the form of personalized digital nudges designed to influence the user in a manner that furthers a savings goal.