Systems and methods for generating dynamic conversational responses using ensemble prediction based on a plurality of machine learning models

A method for generating dynamic conversational responses using ensemble prediction based on a plurality of machine learning models, the method comprising:

• receiving a first user action related to a conversational interaction;
• determining, based on the first user action a first feature input for a first machine learning model, a second feature input for a second machine learning model, and a third feature input for a third machine learning model, wherein the first machine learning model is trained on user interface image data, the second machine learning model is trained on Weight of Evidence (WOE) data, the third machine learning model is trained on transaction data, a fourth machine learning model comprising an ensemble function that is trained on outputs of the first machine learning model, the second machine learning model, and the third machine learning model, and wherein each of the first feature input, the second feature input, and the third feature input is based on a respective data source;
• inputting the first feature input into the first machine learning model, the second feature input into the second machine learning model, and the third feature input into the third machine learning model to generate a first output from the first machine learning model, a second output from the second machine learning model, and a third output from the third machine learning model;
• inputting the first output, the second output, and the third output into the fourth machine learning model to generate a fourth output for use in generating a dynamic conversational response;
• and
• generating for display, the dynamic conversational response for the conversational interaction.

One or more non-transitory computer-readable media comprising of instructions that, when executed by one or more processors, cause operations comprising:

• receiving a first user action related to a conversational interaction;
• determining, based on the first user action, a first feature input for a first machine learning model, a second feature input for a second machine learning model, and a third feature input for a third machine learning model, wherein the first machine learning model is trained on user interface image data, the second machine learning model is trained on Weight of Evidence (WOE) data, the third machine learning model is trained on transaction data, a fourth machine learning model comprising an ensemble function that is trained on outputs of the first machine learning model, the second machine learning model, and the third machine learning model, and wherein each of the first feature input, the second feature input, and the third feature input is based on a respective data source;
• inputting the first feature input into the first machine learning model, the second feature input into the second machine learning model, and the third feature input into the third machine learning model to generate a first output from the first machine learning model, a second output from the second machine learning model, and a third output from the third machine learning model;
• inputting the first output, the second output, and the third output into the fourth machine learning model to generate a fourth output for use in generating a dynamic conversational response;
• and
• generating for display, the dynamic conversational response for the conversational interaction.