Abstract: A method can include receiving a request associated with an interaction involving a target entity. The method can include receiving identity data about the target entity and historical data about historical interactions associated with the target entity. The method can include providing the identity data to a first machine-learning model to generate first output. The method can include applying the historical data to rules to generate rule outcomes. The method can include providing the rule outcomes to a second machine-learning model to generate second output. The method can include generating a recommendation including a response to the request. The method can include providing a responsive message including a command executable to automatically control the response to the request.

Abstract: A system can generate content recommendations and facilitate interactions using machine-learning. The system can receive a request from a provider entity. The system can receive entity data and interaction data associated with a target entity. The system can generate at least a first graph structure and a second graph structure. The system can generate a linked graph structure based on the first graph structure and the second graph structure. The system can determine among a plurality of operations, one or more target operations to perform on data included in the linked graph structure. The system can execute using a trained machine-learning model, the target operations to generate a content recommendation for facilitating an interaction. The system can provide a responsive message based on the content recommendation usable to facilitate the interaction.

Abstract: A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.

Abstract: A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.

Abstract: A system can be used to provide a responsive message for controlling an interaction dispute. The system can receive tokens from an optical character recognition model. The set of tokens can represent at least evidence data relating to an interaction dispute. The system can determine, using an artificial intelligence model, a first likelihood that represents a similarity between a subset of the tokens and the interaction dispute. The system can determine a second likelihood that traversing to the interaction dispute may result in success. The system can provide the responsive message that can control the interaction dispute based on the first likelihood and the second likelihood. The responsive message can include a response to the interaction dispute.

Abstract: A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.