原始名称记载: BANK OF AMERICA CORPORATION

Systems, computer program products, and methods are described herein for scalable network data transmission within a network environment. An example system receives a plurality of ETL tasks and task characteristics associated with each ETL task. In addition, the example system captures network environment configuration at periodic intervals. Using a machine learning (ML) model, the example system may dynamically determine additional secondary ETL pipelines to process the plurality of ETL tasks. Once the appropriate secondary pipelines are identified, the system executes the ETL tasks using both the primary and the secondary pipelines, ensuring efficient and adaptable task processing within a network environment.

Systems, computer program products, and methods are described herein for dynamically generating trusted networks for electronic devices. The present invention is configured to identify a data transmission attempt from a primary user device; analyze, by a generative artificial intelligent (AI) scanner and prior to allowing the data transmission attempt, the primary user device; determine, by the generative AI scanner, at least one altered property is present on the primary user device; generate, based on the determination at least one altered property is on the primary user device, a root break alert interface component comprising a program identifier of the at least one altered property; and transmit the root break alert interface component to the user device.

A system is provided for processing of resource transfers using a distributed register based peer to peer network. In particular, the system may comprise a peer to peer (“P2P”) mesh network comprising a plurality of computing devices forming a shared communication channel with one another. The system may identify the network connection strengths of each of the plurality of wireless computing devices and subsequently designate one or more devices as the hub(s) of the P2P network. The devices within the P2P network may host an encrypted distributed register for storing and processing network requests. In this regard, devices may submit network requests for processing resource transfers to the distributed register to be executed by the one or more hubs of the P2P network. Once all of the devices leave the P2P network, the distributed register is securely erased.

Systems, computer program products, and methods are described herein for communication data identification and alert generation via a machine learning framework. The present disclosure includes determining a first status of an underlying service, receiving a stream of interaction data from a first user via at least one channel, classifying an interaction via a first machine learning model, generating a first notification signal via a second machine learning model, wherein the second machine learning model may be provided, as inputs, the first status of the underlying service and an interaction pattern associated with the identity, and transmitting the first notification signal via the at least one channel to a first endpoint device associated with the first user.

A system is provided for a self organizing and self correcting autonomic peer to peer network for processing resource transfers. In this regard, the system may restructure the P2P mesh network based on the changes in participating devices within the P2P network, such as when the P2P network is divided into clusters or when clusters merge together. In such embodiments, each cluster may comprise a designated primary device and/or a hub device for processing incoming network requests. The primary device may query the other devices within the cluster for updates and broadcast the updates to each of the other devices within the cluster. The updates may then in turn be stored on each of the devices within the cluster within the distributed register, where the various network requests from the devices may be processed by the hub device of the cluster.