Abstract: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Abstract: Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Abstract: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Abstract: Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Abstract: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Abstract: Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Abstract: Arrangements for synchronously tracking and controlling events across multiple computer systems are provided. In some examples, a user may register with a system and user data may be received. In some arrangements, historical data associated with the user may also be received. Machine learning may be used to analyze the historical data and/or user data and a first recommendation for an item may be generated and transmitted to the user. Upon receiving acceptance of the recommendation, the system may request data from one or more entities. For instance, entity data associated with current inventory, availability of items, layout of locations, and the like, may be received. Based on the received data, a list of items for capture and/or an item capture route may be generated. In some examples, the item capture route may include step-by-step or map-based instructions to capture the items on the list.

Abstract: A centralized database identifier repository may identify databases using a unique identifier, or key tag, for each database. Each identified database may include data relating to one or more specific data elements. The repository may include a variety of data elements. Each data element may be associated with one or more database keys. The repository may be a repository of reference pointers. The repository may facilitate data viewing and data retrieval. A requestor may search for a data element using the centralized repository. The repository may retrieve data relating to a specific data element, from all databases identified by unique identifiers, that include data relating to the data element. The databases' unique identifiers may be encrypted tokens.

Abstract: Aspects of the disclosure relate to systems for processing transactions between two entities. A transaction control platform may determine attributes associated with different transfer channels between the two entities. Based on the determined attributes, the transaction control platform may determine a transfer channel to be used for transmitting a message corresponding to the transaction. The transaction control platform may use machine learning algorithms to identify an optimal transfer channel that meets particular desired factors.

Abstract: Aspects of the disclosure relate to systems for processing transactions between two entities. A transaction control platform may determine attributes associated with different transfer channels between the two entities. Based on the determined attributes, the transaction control platform may determine a transfer channel to be used for transmitting a message corresponding to the transaction. The transaction control platform may use machine learning algorithms to identify an optimal transfer channel that meets particular desired factors.

Abstract: Aspects of the disclosure relate to processing systems that implement an enhanced reality device to facilitate transactions at a screen-less automated teller machine (ATM). A computing platform may receive a request to initiate a transaction with a screen-less automated ATM. Based on the request to initiate the transaction, the computing platform may send pre-transaction interface information and commands directing the enhanced reality device to generate a pre-transaction interface. The computing platform may receive an authentication request from the screen-less ATM indicating its availability and requesting authentication information. In response to validating the authentication information, the computing platform may generate transaction interface information. The computing platform may send the transaction interface information to the enhanced reality device. The computing platform may receive a transaction completion indication from the screen-less ATM.

Abstract: Aspects of the disclosure relate to providing augmented reality user interfaces and controlling automated systems based on user activity information and pre-staging information. A computing platform may receive, from a client user device, a trip start notification indicating that a user of the client user device is initiating a trip to an enterprise center. In response to receiving the trip start notification, the computing platform may generate a pre-staging augmented reality user interface for a client augmented reality device linked to the client user device. Thereafter, the computing platform may receive pre-staging information identifying one or more events to be performed at the enterprise center when the user of the client user device arrives at the enterprise center. The computing platform may generate one or more pre-staging commands based on the pre-staging information and may send these commands to one or more systems associated with the enterprise center.

Abstract: A method for securely launching a temporary financial system nested within a permanent financial system, for a locale impacted by a disaster event is provided. The method may include receiving profile-data associated with a new-quasi-user. The method may include storing the profile-data in an account profile. The method may further include analyzing the profile-data. The method may further include authenticating the new-quasi-user based on the analyzing of the profile-data. The method may further include enabling the new-quasi-user, access to a subset of services from the permanent financial system. The enabling may include simulating the temporary financial system to mimic the appearance of the permanent financial system. The method may further include filtering and separating, via a secure information filter, the temporary system from the permanent system thereby limiting the new-quasi-user, access to the permanent financial system.

Abstract: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Abstract: Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Abstract: A centralized database identifier repository may identify databases using a unique identifier, or key tag, for each database. Each identified database may include data relating to one or more specific data elements. The repository may include a variety of data elements. Each data element may be associated with one or more database keys. The repository may be a repository of reference pointers. The repository may facilitate data viewing and data retrieval. A requestor may search for a data element using the centralized repository. The repository may retrieve data relating to a specific data element, from all databases identified by unique identifiers, that include data relating to the data element. The databases' unique identifiers may be encrypted tokens.

Abstract: Arrangements for providing multi-channel tracking and control are provided. In some examples, a request for a quantity of product may be received. The request may be processed and the order transmitted to a destination. During shipping, tracking of the order may be performed to determine whether an order has arrived at a destination. In some examples, quality data may be captured at the destination to determine whether a portion of the order was damaged or destroyed during shipping. In some arrangements, upon detecting delivery of the order the system may generate one or more instructions to update an inventory of the requesting entity based on delivery of the order. In some examples, machine learning may be used to predict when an entity may submit a subsequent request for product, a recommended amount of product, and the like. This information may be transmitted to a requesting entity to simplify future processing requests.

Abstract: Aspects of the disclosure relate to processing systems that provide a teller assistant experience using enhanced reality interfaces. The computing platform may initiate a video call with an enhanced reality device, which may cause the enhanced reality device to display a user of the computing platform in a portion of a teller assistance experience interface and may cause the computing platform to display a video stream corresponding to a user of the enhanced reality device. The computing platform may receive a request to generate a teller experience notification based on the video stream. The computing platform may generate commands directing an enhanced reality teller control platform to generate the teller experience notification. The computing platform may send, to the enhanced reality teller control platform, the commands, which may cause the enhanced reality teller control platform to direct the enhanced reality device to display the teller experience notification.

Abstract: Aspects of the disclosure relate to systems for processing transactions between two entities. A transaction control platform may determine attributes associated with different transfer channels between the two entities. Based on the determined attributes, the transaction control platform may determine a transfer channel to be used for transmitting a message corresponding to the transaction. The transaction control platform may use machine learning algorithms to identify an optimal transfer channel that meets particular desired factors.

Abstract: Aspects of the disclosure relate to systems for processing transactions between two entities. A transaction control platform may determine attributes associated with different transfer channels between the two entities. Based on the determined attributes, the transaction control platform may determine a transfer channel to be used for transmitting a message corresponding to the transaction. The transaction control platform may use machine learning algorithms to identify an optimal transfer channel that meets particular desired factors.