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: A computer system for optimizing an application for monitoring operation of a network is provided. The application may utilize Artificial Intelligence (AI). The computer system may be configured to detect a network transaction volume level that exceeds a first threshold level. In response to a detection of the network transaction volume level that exceeds a first threshold level, the system may utilize the AI to determine whether to implement one or more over-threshold-level mitigation techniques. When the AI instructs the processor to implement the one or more over-threshold-level mitigation techniques, the system may query the AI for an instruction directing an implementation of the over-threshold-level mitigation techniques.

Abstract: A system includes first and second subsystems. The first subsystem receives a validation number request, transmitted by a first device in response to the entry of an account number into a first field of a webpage. In response to receiving the request, the first subsystem randomly generates the validation number, stores a copy in memory, and transmits it to a second device. The second subsystem receives a transaction request that includes the validation number from the first device, and transmits the received number to the first subsystem. The first device transmitted the request in response to the second device receiving the validation number and its subsequent entry into a second field of the webpage displayed on the first device. In response to receiving the validation number, the first subsystem determines that it matches the stored copy and transmits a message to the second subsystem authorizing the 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 method is provided to reduce the number of duplicates of each document that is stored within entity databases. The method may include creating discrete links and/or pointers to the location of the document already stored within an entity. The method may also include separating the document into different classification levels. The method may include storing the different parts of the documents in different locations within the entity.

Abstract: Arrangements for secure user authentication through hardware analysis and monitoring are provided. In some aspects, a computing platform may receive, from one or more hardware devices, user interaction data. The computing platform may establish a baseline indicating an expected usage pattern of the user. The computing platform may receive, from the one or more hardware devices, subsequent user interaction data and determine whether the subsequent data is unauthorized. Based on the subsequent data not being unauthorized, the computing platform may calculate an updated baseline in real-time as the subsequent data is received. The computing platform may monitor for additional user interaction data from the one or more hardware devices and compare the additional user interaction data with the updated baseline. In response to the additional user interaction data deviating from the updated baseline by a predetermined threshold, the computing platform may identify an anomaly and transmit a security notification.

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: Arrangements for providing multi-party exchange functions are provided. In some aspects, a request for exchange may be received by a computing platform. The request for exchange may include identification of parties involved in the exchange, identification of goods, services, property, or the like, involved in the exchange, and the like. In some examples, the computing platform may determine a value of property, goods, or services associated with the exchange. The computing platform may request additional exchange data from one or more other parties. For instance, data associated with the exchange and another party to the exchange may be requested and received. In some examples, unique exchange identifiers may be generated linking each party to the exchange to the goods, services or property being exchange, a value, or the like. An indication of acceptance may be received and one or more exchange processing functions may be executed.

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 method is provided to reduce the number of duplicates of each document that is stored within entity databases. The method may include creating discrete links and/or pointers to the location of the document already stored within an entity. The method may also include separating the document into different classification levels. The method may include storing the different parts of the documents in different locations within the entity.

Abstract: A computer system for optimizing an application for monitoring operation of a network is provided. The application may utilize Artificial Intelligence (AI). The computer system may be configured to detect a network transaction volume level that exceeds a first threshold level. In response to a detection of the network transaction volume level that exceeds a first threshold level, the system may utilize the AI to determine whether to implement one or more over-threshold-level mitigation techniques. When the AI instructs the processor to implement the one or more over-threshold-level mitigation techniques, the system may query the AI for an instruction directing an implementation of the over-threshold-level mitigation techniques.

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: Arrangements for providing multi-party exchange functions are provided. In some aspects, a request for exchange may be received by a computing platform. The request for exchange may include identification of parties involved in the exchange, identification of goods, services, property, or the like, involved in the exchange, and the like. In some examples, the computing platform may determine a value of property, goods, or services associated with the exchange. The computing platform may request additional exchange data from one or more other parties. For instance, data associated with the exchange and another party to the exchange may be requested and received. In some examples, unique exchange identifiers may be generated linking each party to the exchange to the goods, services or property being exchange, a value, or the like. An indication of acceptance may be received and one or more exchange processing functions may be executed.

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 detecting usage issues on enterprise systems and dynamically providing user assistance. In some embodiments, a computing platform may receive, from an automated teller system, user presence information indicating that a user has been detected at an automated teller machine associated with the automated teller system. Subsequently, the computing platform may identify, based on the user presence information received from the automated teller system, that the user has a need for assistance with the automated teller machine associated with the automated teller system. In response to identifying that the user has the need for assistance, the computing platform may generate and send one or more commands directing the automated teller machine associated with the automated teller system to execute one or more automated assistance actions corresponding to the need for assistance.

Abstract: Aspects of the disclosure relate to detecting usage issues on enterprise systems and dynamically providing user assistance. In some embodiments, a computing platform may receive, from an automated teller system, user presence information indicating that a user has been detected at an automated teller machine associated with the automated teller system. Subsequently, the computing platform may identify, based on the user presence information received from the automated teller system, that the user has a need for assistance with the automated teller machine associated with the automated teller system. In response to identifying that the user has the need for assistance, the computing platform may generate and send one or more commands directing the automated teller machine associated with the automated teller system to execute one or more automated assistance actions corresponding to the need for assistance.

Abstract: This application describes apparatus and methods for using edge-computing to control resource distribution among access channels, such as a retail banking center. Edge-nodes may be configured to move a product display in response to detected or expected customer traffic flow in or near a retail location. Edge-nodes may be configured to redirect resources provided by a cloud computing environment to or away from the retail location. Based on customer traffic flow, edge-nodes may direct customers/resources to a retail location and ensure the retail location provides a predetermined quality of service.

Abstract: Various aspects of the disclosure relate to automated generation and electronic delivery of digital representations of guaranteed financial instruments. A check generation service platform may process personal financial instrument images, along with user account information, to generate electronic representations of guaranteed financial products. Delivery of the guaranteed financial product may be performed as an electronic message comprising one or more of a bar code, a QR code, or an electronic token, and an electronic image that may be executed by a third-party computing platform.

Abstract: Arrangements for providing multi-party exchange functions are provided. In some aspects, a request for exchange may be received by a computing platform. The request for exchange may include identification of parties involved in the exchange, identification of goods, services, property, or the like, involved in the exchange, and the like. In some examples, the computing platform may determine a value of property, goods, or services associated with the exchange. The computing platform may request additional exchange data from one or more other parties. For instance, data associated with the exchange and another party to the exchange may be requested and received. In some examples, unique exchange identifiers may be generated linking each party to the exchange to the goods, services or property being exchange, a value, or the like. An indication of acceptance may be received and one or more exchange processing functions may be executed.

Abstract: A system includes first and second subsystems. The first subsystem receives a validation number request, transmitted by a first device in response to the entry of an account number into a first field of a webpage. In response to receiving the request, the first subsystem randomly generates the validation number, stores a copy in memory, and transmits it to a second device. The second subsystem receives a transaction request that includes the validation number from the first device, and transmits the received number to the first subsystem. The first device transmitted the request in response to the second device receiving the validation number and its subsequent entry into a second field of the webpage displayed on the first device. In response to receiving the validation number, the first subsystem determines that it matches the stored copy and transmits a message to the second subsystem authorizing the transaction.