Abstract: Systems, computer program products, and methods are described herein for predicting, detecting, and preventing unauthorized access events associated with electronic systems. The systems, computer program products, and methods may be configured to receive, from a first automated teller machine, an alert message including information associated with a first unauthorized access event occurring at the first automated teller machine and determine, based on the information in the alert message, a location of the first automated teller machine. The systems, computer program products, and methods may be configured to identify a second automated teller machine proximate the location of the first automated teller machine and provide, to the second automated teller machine, one or more commands to be executed by the second automated teller machine to prevent a second unauthorized access event from occurring at the second automated teller machine.

Abstract: Systems, methods, and apparatus are provided for authenticating avatar activity in a metaverse environment. A metaverse application may enable interactions between an agent avatar and a customer avatar in a secure bidirectional metaverse session. Customer identity may be verified using meta-information associated with avatar appearance or behavior, device data, and/or methods of avatar control. The agent avatar and customer avatar may view a virtual representation of a defective check in the metaverse environment. The customer avatar may sign the virtual check in the metaverse environment. The signature may be captured and reproduced on a paper check by a device interfacing with the metaverse application. The metaverse application may store technical logs of the metaverse session for future reference.

Abstract: Systems, methods, and apparatus are provided for secure check exception remediation in a metaverse environment. A metaverse application may enable interactions between an agent avatar and a customer avatar in a secure bidirectional metaverse session. Customer identity may be verified using meta-information associated with avatar appearance or behavior, device data, and/or methods of avatar control. The agent avatar and customer avatar may view a virtual representation of a defective check in the metaverse environment. The customer avatar may sign the virtual check in the metaverse environment. The signature may be captured and reproduced on a paper check by a device interfacing with the metaverse application. The metaverse application may store technical logs of the metaverse session for future reference.

Abstract: Arrangements for transaction adapter control are provided. In some aspects, a user device may connect to a network in a current geographic location. The user computing device may detect that a change in geographic location has occurred based on the current geographic location. Responsive to detecting that the change in geographic location has occurred, a mobile transaction adapter associated with or unique to the current geographic location may be downloaded to the user computing device. The mobile transaction adapter may be a plug-in component to a mobile payment application on the user computing device. A request to process a transaction may be received via the mobile payment application on the user device. Upon receiving the request to process the transaction, the mobile transaction adapter may be activated and the request may be routed to the mobile transaction adapter. The requested transaction may then be processed via the mobile transaction adapter.

Abstract: A system includes a memory and a processor. The processor is configured to determine that a user has ceased using a first device while the user was in an active user session with an application. The processor is further configured to determine a last user interface with which the user interacted on the first device and to determine user-supplied information from the last user interface. The processor is further configured to determine that the user is attempting to start a new user session with the application on a second device, and in response, display a message on the second device inquiring if the user wishes to continue with the last user interface. The processor is further configured to display a new user interface on the second device that corresponds to the last user interface and display the user-supplied information from the last user interface in the new user interface.

Abstract: A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.

Abstract: A system includes a memory and a processor. The processor is configured to determine that a user has ceased using a first device while the user was in an active user session with an application. The processor is further configured to determine a last user interface with which the user interacted on the first device and to determine user-supplied information from the last user interface. The processor is further configured to determine that the user is attempting to start a new user session with the application on a second device, and in response, display a message on the second device inquiring if the user wishes to continue with the last user interface. The processor is further configured to display a new user interface on the second device that corresponds to the last user interface and display the user-supplied information from the last user interface in the new user interface.

Abstract: Arrangements for transaction adapter control are provided. In some aspects, a user device may connect to a network in a current geographic location. The user computing device may detect that a change in geographic location has occurred based on the current geographic location. Responsive to detecting that the change in geographic location has occurred, a mobile transaction adapter associated with or unique to the current geographic location may be downloaded to the user computing device. The mobile transaction adapter may be a plug-in component to a mobile payment application on the user computing device. A request to process a transaction may be received via the mobile payment application on the user device. Upon receiving the request to process the transaction, the mobile transaction adapter may be activated and the request may be routed to the mobile transaction adapter. The requested transaction may then be processed via the mobile transaction adapter.

Abstract: Arrangements for intelligent routing of transactions via a transaction adapter are provided. In some aspects, a request for a system selected mobile transaction adapter may be received from a user computing device in a current geographic location. Responsive to receiving the request, a computing platform may generate a plurality of requests for external entity data. The computing platform may receive entity data from the one or more external entities. In some examples, the entity data from the one or more external entities may be analyzed using machine learning. For instance, one or more machine learning models may be executed to identify, based on the data, an optimal or preferred service provider and/or mobile transaction adapter associated with the identified service provider. The identified mobile transaction adapter may then be transmitted to the user computing device for installation.

Abstract: Systems, computer program products, and methods are described herein for predicting, detecting, and preventing unauthorized access events associated with electronic systems. The present invention may be configured to receive, from a first automated teller machine, an alert message including information associated with a first unauthorized access event occurring at the first automated teller machine and determine, based on the information in the alert message, a location of the first automated teller machine. The present invention may be configured to identify a second automated teller machine proximate the location of the first automated teller machine and provide, to the second automated teller machine, one or more commands to be executed by the second automated teller machine to prevent a second unauthorized access event from occurring at the second automated teller machine.

Abstract: A system for intelligent data transfer is provided. The system is configured to: collect sensor data from a plurality of user devices, the plurality of user devices being connected to a device gateway in an edge layer of the network; combine the collected sensor data with contextual data stored in a contextual device database, wherein the contextual data comprises device usage data and user data; generate a data transfer rule set for governing data transfer from the plurality of user devices over the network based on the combined data; calculate a data configuration flow for the plurality of user devices based on the data transfer rule set; and execute the data configuration flow to control a flow of the sensor data transferred from the device gateway to an application server in a platform layer.

Abstract: Systems for performing intelligent interactive voice recognition functions are provided. In some aspects, natural language data may be received from a plurality of users. The natural language data may be used to generate a plurality of user-specific machine learning datasets. Subsequent natural language input data including a user query may be received. The query may be analyzed to identify the user and a user-specific machine learning dataset associated with the user may be identified. The natural language data may be processed to remove noise associated with the data and may be further processed using the identified user-specific machine learning dataset to interpret the query of the user and generate an output. The output may be transmitted to the user and feedback data may be received from the user. The user-specific machine learning dataset may then be validated and/or updated based on the feedback data.

Abstract: Systems for performing intelligent interactive voice recognition functions are provided. In some aspects, natural language data may be received from a plurality of users. The natural language data may be used to train a machine learning model. After training the machine learning model, additional or subsequent natural language input data may be received. The natural language data may include a user query, such as a request to obtain information from the system, to process a transaction, or the like. The natural language data may be processed to remove noise associated with the audio data. The data may then be further processed using the machine learning model to interpret the query of the user and generate an output. The output may be transmitted to the user and feedback data may be received from the user. The user-specific machine learning dataset may then be validated and/or updated based on the feedback data.

Abstract: Embodiments of the present invention provide a system for intelligent processing, routing, and distributing electronic resource requests.

Abstract: Aspects of the disclosure relate to an automatic teller machine (ATM) network. A computing device may monitor operations in the ATM network. The computing device may determine an ATM refill route for a cash truck based on the monitoring the ATM network and/or based on refill notifications sent by an ATM. An ATM may communicate with a second ATM to pre-stage a user transaction at the second ATM.

Abstract: A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.

Abstract: A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.

Abstract: Aspects of the disclosure relate to an automatic teller machine (ATM) network. A computing device may monitor operations in the ATM network. The computing device may determine an ATM refill route for a cash truck based on the monitoring the ATM network and/or based on refill notifications sent by an ATM. An ATM may communicate with a second ATM to pre-stage a user transaction at the second ATM.

Abstract: A system for intelligent data transfer is provided. The system is configured to: collect sensor data from a plurality of user devices, the plurality of user devices being connected to a device gateway in an edge layer of the network; combine the collected sensor data with contextual data stored in a contextual device database, wherein the contextual data comprises device usage data and user data; generate a data transfer rule set for governing data transfer from the plurality of user devices over the network based on the combined data; calculate a data configuration flow for the plurality of user devices based on the data transfer rule set; and execute the data configuration flow to control a flow of the sensor data transferred from the device gateway to an application server in a platform layer.

Abstract: A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user's preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.