Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive, via a network, a multiple positions corresponding to multiple physical locations of a consumer device, may receive, via the network, a position of a physical location of a financial transaction device, and may receive a request for a financial transaction, and may determine a transaction authorization based on a path indicated by the multiple positions and the position of the physical location of the financial transaction device. For example, a path and/or track may be established via periodical reports of the position information from the consumer device, which may provide and/or indicate an authenticity metric and/or a validity metric to a current position attribute when the current position attribute is utilized in a financial transaction.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Machine learning models, semantic networks, adaptive systems, artificial neural networks, convolutional neural networks, and other forms of knowledge processing systems are disclosed. An ensemble machine learning system is coupled to a graph module storing a graph structure, wherein a collection of entities and the relationships between those entities forms nodes and connection arcs between the various nodes. A hotfile module and hotfile propagation engine coordinate with the graph module or may be subsumed within the graph module, and implement the various hot file functionality generated by the machine learning systems.

Abstract: Aspects of the disclosure relate to a system to monitor devices for unauthorized access of an external account. A computing platform may receive, via a communication interface, and from a first user device, metadata comprising first user device identification information and accessed account information. The computing platform may determine, based on the first user device identification information, whether the first user device is one of a plurality of internal user devices. The computing platform may identify, based on the accessed account information, an action indicating the first user device is accessing an account. The computing platform may determine, based on the action and the accessed account information, whether the action is authorized. The computing platform may receive information indicating unauthorized use of the account by the first user device. The computing platform may transmit the information indicating unauthorized use of the account by the first user device.

Abstract: Aspects of the disclosure relate to controlling device data collectors using omni-collection techniques. A computing platform may receive configuration settings associated with a first collector and a second collector. Subsequently, the computing platform may generate one or more configuration commands for a native collector based on the configuration settings. The computing platform may send, to a client interface computing platform associated with the native collector, the one or more configuration commands generated for the native collector. Thereafter, the computing platform may receive state information collected by the native collector based on the one or more configuration commands generated for the native collector.

Abstract: In one or more embodiments, one or more systems, processes, and/or methods may receive, via a network, a multiple positions corresponding to multiple physical locations of a consumer device, may receive, via the network, a position of a physical location of a financial transaction device, and may receive a request for a financial transaction, and may determine a transaction authorization based on a path indicated by the multiple positions and the position of the physical location of the financial transaction device. For example, a path and/or track may be established via periodical reports of the position information from the consumer device, which may provide and/or indicate an authenticity metric and/or a validity metric to a current position attribute when the current position attribute is utilized in a financial transaction.

Abstract: Aspects of the disclosure relate to a system to monitor devices for unauthorized access of an external account. A computing platform may receive, via a communication interface, and from a first user device, metadata comprising first user device identification information and accessed account information. The computing platform may determine, based on the first user device identification information, whether the first user device is one of a plurality of internal user devices. The computing platform may identify, based on the accessed account information, an action indicating the first user device is accessing an account. The computing platform may determine, based on the action and the accessed account information, whether the action is authorized. The computing platform may receive information indicating unauthorized use of the account by the first user device. The computing platform may transmit the information indicating unauthorized use of the account by the first user device.

Abstract: Aspects of the disclosure relate to controlling device data collectors using omni-collection techniques. A computing platform may receive configuration settings associated with a first collector and a second collector. Subsequently, the computing platform may generate one or more configuration commands for a native collector based on the configuration settings. The computing platform may send, to a client interface computing platform associated with the native collector, the one or more configuration commands generated for the native collector. Thereafter, the computing platform may receive state information collected by the native collector based on the one or more configuration commands generated for the native collector.