Predictive response from conversational flow

A system for providing user assist by a bot in a digital application, said system comprising:

• a back-end server operating the digital application and including:
• at least one processor for processing data and information;
• a communications interface communicatively coupled to the at least one processor;
• and
• a memory device storing data and executable code that, when executed, causes the at least one processor to:
• transform, via data cleaning, ingested data into a standardized training format for training machine learning models;
• train, using training test data in the standardized training format, an unsupervised neural network utilizing interconnected nodes, the unsupervised neural network being trained to cause a bot to identify process flows, cause the bot to identify information provided with a current process flow and cause the bot to anticipate questions to address one or more issues arising during the current process flow, the training including:
• inserting the training test data into an iterative training and testing loop to predict a target variable;
• and
• repeatedly predicting the target variable during multiple versions of the training and testing loop, each version of the multiple versions having differing weights applied to one or more nodes in one or more layers of the unsupervised neural network, each of the differing weights being updated with each of the multiple versions of the training and testing loop to reduce error in predicting the target variable, which improves predictability of the target variable and functionality of the unsupervised neural network;
• deploy the unsupervised neural network;
• allow a user to enter information and data into a certain banking process flow in the application;
• cause the bot to identify the current process flow the user is entering information into and where in the current process flow the user is using the deployed unsupervised neural network;
• cause the bot to identify what information the user is entering into the current process flow using the deployed unsupervised neural network;
• and
• cause the bot to anticipate a specific question the user may have about the information being entered into the current process flow the one or more issues arising during the current process flow and answer the question before the user asks it using the deployed unsupervised neural network to automatically reduce delays associated with resolving the one or more issues within the current process flow to streamline computer processing for the current process flow;
• and
• transmit feedback data to the unsupervised neural network, wherein the feedback data is used to adjust subsequent predictions generated by the unsupervised neural network.

A system for providing user assist by a bot in a digital application, said system comprising:

• a back-end server operating the digital application and including:
• at least one processor for processing data and information;
• a communications interface communicatively coupled to the at least one processor;
• and
• a memory device storing data and executable code that, when executed, causes the at least one processor to:
• transform, via data cleaning, ingested data into a standardized training format for training machine learning models;
• train, using training test data in the standardized training format, an unsupervised neural network utilizing interconnected nodes, the unsupervised neural network being trained to cause a bot to identify process flows, cause the bot to identify information provided with a current process flow and cause the bot to recognize incorrect information to address one or more issues arising during the current process flow, the training including:
• inserting the training test data into an iterative training and testing loop to predict a target variable;
• and
• repeatedly predicting the target variable during multiple versions of the training and testing loop, each version of the multiple versions having differing weights applied to one or more nodes in one or more layers of the unsupervised neural network, each of the differing weights being updated with each of the multiple versions of the training and testing loop to reduce error in predicting the target variable, which improves predictability of the target variable and functionality of the unsupervised neural network;
• deploy the unsupervised neural network;
• allow a user to enter information and data into a certain banking process flow in the application;
• cause the bot to identify the current process flow the user is entering information into and where in the current process flow the user is using the deployed unsupervised neural network;
• automatically cause the bot to identify what information the user is entering into the current process flow using the deployed unsupervised neural network;
• cause the bot to recognize incorrect information entered by the user into the current process flow and provide the correct information using the deployed unsupervised neural network;
• and
• cause the bot to anticipate a specific question the user may have about the information being entered into the current process flow and answer the question before the user asks it to automatically reduce delays associated with resolving the one or more issues within the current process flow to streamline computer processing for the current process flow;
• and
• transmit feedback data to the unsupervised neural network, wherein the feedback data is used to adjust subsequent predictions generated by the unsupervised neural network.