Bank of America Corporation

Saritha Vrittamani

Manu Jacob Kurian

Paul Roscoe

Jayachandra Varma

Gopikrishna Nemalikanti

Jerzy Miernik

Crystal M Sundaramoorthy

Jeff T Burch

Karl W Thomas

Jo-Ann Taylor

Srinivasa D Madireddi

Timothy Lamonte Atwell

Michael S Karafotis

Jason T Findley

Patrick Nicholas Lawrence

Kevin A Delson

Gilbert M Gatchalian

Lixian Huang

Floyd Franklin Rock, Jr

Renee Marie Melin

Mitchell T Boston

Qishan Cai

Keesara Himabindu

Angela Cain

Systems and methods for batch multiplexing a plurality of transactions are provided. A system may include a machine-learning ('ML') based engine configured to receive instructions to execute a first transaction, derive a plurality of sets of delay parameters for the first transaction, and display the sets of delay parameters on a graphical user interface ('GUI') associated with a sender of the instructions. The engine may be configured to receive a selection of a set of delay parameters, and, based on the selection, append the first transaction to one of a plurality of pending transaction queues. The engine may be further configured to, within a maximum time window, combine the transactions in the pending transaction queue to create a single multiplexed transaction, and execute the multiplexed transaction. The engine may also be configured to demultiplex the multiplexed transaction after it is executed.

Batch transaction multiplexing engine

A system that includes a tagging engine and a routing engine. The tagging engine is configured to link a data element with an access control tag. The tagging engine is configured to apply context rules to the access control tag array based on the content of the data element to change the access control tag value for one or more of the access control tags. The tagging engine sends the data element with the access control tag array to a target network node within an end user group. The routing engine is configured to identify an access control tag value in the access control tag array corresponding with the end user group and to forward the data element to the target network node in response to determining that the access control value is greater than or equal to the access control level associated with the end user group.

Access control value systems

Aspects of the disclosure relate to transferring data using a smart reconciliation system. A computing platform may receive, from a smart reconciliation node, a data file. Thereafter, the computing platform may retrieve, based on the data file, a data file profile indicating historical information associated with the data file. Then, the computing platform may determine an issue associated with the data file. Subsequently, the computing platform may identify, based on the issue associated with the data file, a data file level error indicating a level where the issue occurred. Next, the computing platform may generate, based on the data file level error, one or more commands directing the smart reconciliation node to execute an automated response for the issue associated with the data file. Then, the computing platform may transmit the one or more commands. Afterwards, the computing platform may update the data file profile.

Transferring data using a smart reconciliation system

Systems and methods for self-referencing utilization calculation and dynamic resource allocation for digital transmissions are provided. Systems may include a target resource module configured to derive and store a target resource allocation for each sender from a plurality of senders. Systems may include a cost-calculation engine configured to generate a resource cost estimate for a transmission based on metadata associated with the transmission, and embed the resource cost estimate in the metadata. Systems may also include a resource allocation module configured to allocate digital resources for the transmission based on the metadata. Allocating digital resources for the transmission may reserve the digital resources for the transmission and prevent overloading of the digital resources.

Systems and methods for providing self-referencing utilization calculation and dynamic resource allocation based thereon for digital transmissions

Intelligent delivery/transmission of data within a secure transmission path of a distributed computing network. A plurality of logical switches are disposed throughout a secure transmission path between a source and target. A controller is configured to control a timing for delivery and/or routing of the data packets to the target apparatus by activating and deactivating the logical switches. In addition, activation of two or more switches provides for isolating data, such that, inline processing (e.g., security checks or the like) can be performed on the data.

Intelligent delivery of data packets within a network transmission path based on time intervals

A system for creating a synthetic voice identifier may include a plurality of synthesized voice authorization (SVA) devices and a biometric combinatory device (BCD). The SVAs may be communicatively coupled to the BCD via a network and may communicate utilizing a markup language. The SVA devices may capture an audio signal of a voice of a user, modify the audio signal with a randomized audio frequency signal to generate a modified audio signal, and communicate, the modified audio signal as a synthesized voice signal associated with the user. The BCD may receive biometric information corresponding to a user, the biometric information comprising at least audio information associated with a voice of the user, receive, at an integration module, location information corresponding to a location of the user, combine, the location information and audio signal information associated with the user to generate a synthesized voice identifier associated with the user, and communicate the synthesized voice identifier to a remote device for use in an authentication process of the user.

Synthesized voice authentication engine

Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome technical challenges associated with transferring data files in a distributed network having multiple data sources and multiple data destinations. In some examples, a computer system, device, platform, or the like is configured to control the data transfers between other computing systems, devices, platforms, or the like in a computing environment. Controlling the data transfer includes, in some examples, detecting the data transfer between a data source and a data destination, evaluating a data transfer condition associated with the data transfer, determining a data transfer instruction based, at least in part, on the data transfer condition, and providing the data transfer instruction to the data source. The data source then transfers the data to the data destination in accordance with the data transfer instruction.

Data transfer control

Systems for dynamic authentication are provided. In some examples, a system may receive a request to process an event. In some examples, the request to process the event may include additional details associated with the event. The system may initiate dynamic authentication functions and may retrieve data from a plurality of sources. In some examples, the data from the plurality of sources may be analyzed using machine learning to dynamically generate authentication data, such as one or more authentication questions. The system may also generate one or more corresponding responses or answers to the one or more authentication questions. In some examples, the one or more authentication questions may be transmitted to a user device or other device and may be displayed to the user. The user may provide authentication response data that may be analyzed by the system to determine whether it matches the generated response or answer. If so, the user may be authenticated and/or the event may be processed.

Intelligent dynamic authentication system

Systems for dynamically controlling data transmissions are provided. In some examples, a system may receive data from one or more computer systems. Data associated with an event may be extracted and the extracted data may be compared to one or more machine learning datasets to determine a likelihood that an issue associated with a computer system may occur. The system may determine whether the likelihood is greater than a predetermined threshold. If so, a modification to avoid the potential issue or mitigate an impact may be identified. The modification may be transmitted to and executed by the computer system to modify one or more aspects of the computer system. In some examples, one or more efficiency modifications may be identified based on the comparison of the extracted data to the machine learning datasets. The identified efficiency modification may be transmitted to and executed by the computer system to modify one or more aspects of the computer system.

Data processing system with machine learning engine to provide dynamic data transmission control functions

A system includes a request processing system which enables the system to fulfill data requests by grouping and combining requested data elements, while conserving computing resources and increasing security of the requested data elements associated during storage and transmission over a network. The system is configured to identify one or more groups of data elements associated with one or more identifiers, each data element in a group associated with a component of an identifier. The system combines each of the one or more groups of data elements based on a unique association between the components of each identifier. The system subsequently removes the sub-wrappers from the data elements in the one or more groups of data elements, and sends the combined one or more groups of data elements to relevant requestors.

Request processing system using a combining engine

A method for throttling an allocation of resources for processing digital transmissions is provided. The method may include receiving initial digital transmissions from a plurality of client systems and analyzing the initial digital transmissions. The analyzing may determine a cluster of digital transmissions, an amount of resources for processing the cluster and a time-window for processing the cluster. The method may further include allocating the determined amount of resources to each of the clusters. The method may also include processing recurrently, for each of the systems, digital transmissions received after the initial digital transmissions. Concurrently with the periodic processing, the method may include, for each client system, monitoring digital transmissions including recording a transmission log, identifying changes in the log, assigning a credibility score and throttling, dynamically, the amount of resources allocated, based on the credibility score and the identified change.

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Plano, TX, United States of America

Saritha Vrittamani