Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: Arrangements for providing frictionless unauthorized activity detection and user authentication are provided. In some aspects, user data, such as transaction data may be received and used to train a machine learning model. In some examples, the machine learning model may be executed to generate one or more expected user patterns. In some arrangements, a request for transaction may be received. The request for transaction may include transaction details. In response, the system may request current geo-location data of a user. In some examples, the transaction details and geo-location data may be analyzed (e.g., compared to the expected user patterns) to generate an authentication output. The authentication output may then be transmitted to one or more systems to process the requested transaction, prevent transaction processing, or the like.

Abstract: A device configured to receive a data transfer initiation request from a first user and to identify a user profile that is associated with a first user identifier for the first user. The device is further configured to obtain an account number and a routing number for the first user from the user profile. The device is further configured to obtain routing instructions for a service provider based on a service provider identifier. The device is further configured to generate a data transfer request that includes the account number for the first user, the routing number for the first user, a second user identifier for a second user, a data type identifier, and a data transfer type identifier. The device is further configured to send the data transfer request to the service provider in accordance with the routing instructions for the service provider.

Abstract: Aspects of the disclosure relate to a decision model to open and close enterprise facilities. A computing platform may receive one or more attributes associated with an operation of an enterprise facility. Subsequently, the computing platform may identify, based on the one or more attributes, a business operation rule for the enterprise facility. Then, the computing platform may determine, based on the one or more attributes and the business operation rule, an operational parameter for the enterprise facility. Then, the computing platform may compare the operational parameter to a threshold parameter, where the threshold parameter may be indicative of whether to keep the enterprise facility open for business. Then, the computing platform may, upon a determination that the operational parameter fails to exceed the threshold parameter, determine to temporarily close the enterprise facility. Then, the computing platform may redistribute user traffic to one or more open customer resource facilities.

Abstract: Aspects of the disclosure relate to a decision model to open and close enterprise facilities. A computing platform may receive one or more attributes associated with an operation of an enterprise facility. Subsequently, the computing platform may identify, based on the one or more attributes, a business operation rule for the enterprise facility. Then, the computing platform may determine, based on the one or more attributes and the business operation rule, an operational parameter for the enterprise facility. Then, the computing platform may compare the operational parameter to a threshold parameter, where the threshold parameter may be indicative of whether to keep the enterprise facility open for business. Then, the computing platform may, upon a determination that the operational parameter fails to exceed the threshold parameter, determine to temporarily close the enterprise facility. Then, the computing platform may redistribute user traffic to one or more open customer resource facilities.

Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: Aspects of the disclosure relate to a cognitive automation based compliance management system. A computing platform may receive, via a network, an alert notification indicative of a change to a legal framework applicable to an enterprise organization. Subsequently, the computing platform may, in response to the alert notification, detect a task that may be impacted by the change. Then, the computing platform may, upon detecting the task, identify at least one task parameter associated with the task. Then, the computing platform may compare the at least one task parameter with the change to determine whether the task is impacted by the change. Subsequently, the computing platform may, upon a determination that the task is impacted by the change, recommend, via the network, one or more actions associated with the task.

Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: Aspects of the disclosure relate to a decision model to open and close enterprise facilities. A computing platform may receive one or more attributes associated with an operation of an enterprise facility. Subsequently, the computing platform may identify, based on the one or more attributes, a business operation rule for the enterprise facility. Then, the computing platform may determine, based on the one or more attributes and the business operation rule, an operational parameter for the enterprise facility. Then, the computing platform may compare the operational parameter to a threshold parameter, where the threshold parameter may be indicative of whether to keep the enterprise facility open for business. Then, the computing platform may, upon a determination that the operational parameter fails to exceed the threshold parameter, determine to temporarily close the enterprise facility. Then, the computing platform may redistribute user traffic to one or more open customer resource facilities.

Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: Aspects of the disclosure relate to a cognitive automation based compliance management system. A computing platform may receive, via a network, an alert notification indicative of a change to a legal framework applicable to an enterprise organization. Subsequently, the computing platform may, in response to the alert notification, detect a task that may be impacted by the change. Then, the computing platform may, upon detecting the task, identify at least one task parameter associated with the task. Then, the computing platform may compare the at least one task parameter with the change to determine whether the task is impacted by the change. Subsequently, the computing platform may, upon a determination that the task is impacted by the change, recommend, via the network, one or more actions associated with the task.

Abstract: Aspects of the disclosure relate to a cognitive automation based vendor compliance system. A computing platform may monitor, via an enterprise network, use of a software product hosted by an enterprise computing infrastructure. Subsequently, the computing platform may determine a product usage score, where the product usage score is indicative of a number network devices using the software product. Then, the computing platform may determine, for the software product, a product performance score, where the product performance score is indicative of one or more performance issues associated with the software product. Then, the computing platform may compare, for the software product, at least one of: (1) the product usage score to a product usage threshold, and (2) the product performance score to a product performance threshold. Subsequently, the computing platform may recommend, based on the comparing, one or more actions associated with the software product.

Abstract: A method for donating to a charitable organization from a digital donation platform is provided. The method may include identifying a digital transmission from the charitable organization including a request for a donation of funds. The method may include creating a digital account for the charitable organization. The method may include assigning a score for the charitable organization based on a diversity of employees in the organization. When the score is equal to or above a pre-determined threshold, the method may include assigning one of a first tier of digital portals to the charitable organization including a first level of platform oversight over the funds being transferred. When the score is below the pre-determined threshold, the method may include assigning one of a second tier of digital portals to the charitable organization including a second level of platform oversight over the funds being transferred.