A system and method of automated debt management with machine learning is disclosed. An example system may include a data collection circuit to collect information about entities involved in debt transactions, a training data set of outcomes related to the entities, and a training set of debt management activities. The system may also include a condition classifying circuit to classify a condition of at least one of the entities and an automated debt management circuit to manage an action related to a debt. The condition classifying circuit may include a model trained using the training data set of outcomes related to the entities.

A system and method of automated debt management with machine learning is disclosed. An example system may include a data collection circuit to collect information about entities involved in debt transactions, a training data set of outcomes related to the entities, and a training set of debt management activities. The system may also include a condition classifying circuit to classify a condition of at least one of the entities and an automated debt management circuit to manage an action related to a debt. The condition classifying circuit may include a model trained using the training data set of outcomes related to the entities.

A system for selection and configuration of a robotic process includes a data input module to receive a stream of inputs relating to a user engaged in a task of interest, an input analysis module to analyze the stream of inputs and provide a series of timestamped actions and associated action parameters, and a component selection module to select a component of an AI solution for use in an automated robotic process, based on, at least in part, an action of the series of actions, the associated action parameters, or the components ability to simulate one or more of the actions in the series of actions.

A system for selection and configuration of a robotic process includes a data input module to receive a stream of inputs relating to a user engaged in a task of interest, an input analysis module to analyze the stream of inputs and provide a series of timestamped actions and associated action parameters, and a component selection module to select a component of an AI solution for use in an automated robotic process, based on, at least in part, an action of the series of actions, the associated action parameters, or the components ability to simulate one or more of the actions in the series of actions.

A system and method of varied terms and conditions of a subsidized loan includes a data collection circuit structured to receive data related to a plurality of items of collateral; a collateral classification circuit structured to identify, among the plurality of items of collateral, at least one group of related items of collateral, wherein each member of the at least one group shares a common attribute; and a smart contract circuit structured to create a smart lending contract, wherein the smart lending contract defines a subset of items of collateral as security for a set of loans, wherein the subset of items of collateral is selected from the at least one group of related items of collateral.

A system and method of varied terms and conditions of a subsidized loan includes a data collection circuit structured to receive data related to a plurality of items of collateral; a collateral classification circuit structured to identify, among the plurality of items of collateral, at least one group of related items of collateral, wherein each member of the at least one group shares a common attribute; and a smart contract circuit structured to create a smart lending contract, wherein the smart lending contract defines a subset of items of collateral as security for a set of loans, wherein the subset of items of collateral is selected from the at least one group of related items of collateral.

Systems, methods and apparatus for a robotic process automation system for consolidating a set of loans are disclosed herein. An example system may include a set of data collection and monitoring services for collecting information about a set of loans and for collecting a training set of interactions between entities for a set of loan consolidation transactions; an artificial intelligence system that is trained on the training set of interactions to classify a set of loans as candidates for consolidation; and a robotic process automation system that is trained on a set of loan consolidation interactions to manage consolidation of at least a subset of the set of loans on behalf of a party to the consolidation.

Systems, methods and apparatus for a robotic process automation system for consolidating a set of loans are disclosed herein. An example system may include a set of data collection and monitoring services for collecting information about a set of loans and for collecting a training set of interactions between entities for a set of loan consolidation transactions; an artificial intelligence system that is trained on the training set of interactions to classify a set of loans as candidates for consolidation; and a robotic process automation system that is trained on a set of loan consolidation interactions to manage consolidation of at least a subset of the set of loans on behalf of a party to the consolidation.

A method for selection and configuration of an automated robotic process includes receiving a temporal biometric measurement of a worker performing a task, receiving a spatial-temporal environmental input provided to the worker, identifying a type of reasoning used when performing the task partially based on the temporal biometric measurement of the worker, selecting a component of an AI solution to replicate the type of reasoning, and configuring the component of the AI solution based on the spatial-temporal environmental input. The temporal biometric measurement includes a set of spatial-temporal imaging data of a brain of the worker and identifying the type of reasoning includes identifying a set of spatial-temporal neocortical activity patterns of the worker, identifying an active area of a neocortex of the worker; and selecting the component of the AI solution partially based on the identified active area of the neocortex.

A method for selection and configuration of an automated robotic process includes receiving a temporal biometric measurement of a worker performing a task, receiving a spatial-temporal environmental input provided to the worker, identifying a type of reasoning used when performing the task partially based on the temporal biometric measurement of the worker, selecting a component of an AI solution to replicate the type of reasoning, and configuring the component of the AI solution based on the spatial-temporal environmental input. The temporal biometric measurement includes a set of spatial-temporal imaging data of a brain of the worker and identifying the type of reasoning includes identifying a set of spatial-temporal neocortical activity patterns of the worker, identifying an active area of a neocortex of the worker; and selecting the component of the AI solution partially based on the identified active area of the neocortex.