Bank of America Corporation

Rajalakshmi Arumugam

Mahesh Ganesan

Kapil Juneja

Venkatesh Polneedi

Suhasini Mullapudi

Harilakshmi Narayanan

Kiran Subramanyam Darbha

Sreenivas Chintada

Sesharamanujam Padmanabhan

Siddharth Sethia

Nitin Bansal

Anil Garg

Gaurav Kumar Kashyap

Mallapragada Nagaharini

Deshpande Rukmini Ravindra

A system for interaction pattern recognition receives an input primary interaction and accesses clusters indicating interaction group patterns. Each cluster includes a respective primary interaction and secondary interactions linked to that primary interaction. Each cluster is identified by a respective non-fungible token. The system then determines a non-fungible token of the input primary interaction, compares it to the non-fungible tokens of the clusters, selects a first cluster based on a match between the non-fungible token of the input primary interaction and a first non-fungible token identifying the first cluster, determines the secondary interactions in the first cluster as linked to the input primary interaction, retrieves the secondary interactions from the clusters, generates a recommended group of interactions including the input primary interaction and the retrieved secondary interactions, and provides the recommended group of interactions and an indication that the retrieved secondary interactions are linked to the input primary interaction.

Pattern recognition using NLP-based tokenizing and clustering models

A plurality of data traffic patterns are generated based on call logs associated with previous data exchanges between a first microservice entity and a second microservice entity. It is determined based on a data policy that the second microservice entity is not authorized to consume a portion of the data exposed by the first microservice entity. In response, a first data traffic pattern of the plurality of data traffic patterns is identified that is associated with a first known data transfer or a first potential data transfer in which the second microservice entity consumes the portion of the data exposed by the first microservice entity. An alert associated with the first data traffic pattern is generated.

System and method for identifying unauthorized data traffic in a computing network

A system is provided for code base sharing during computing application failure using a distributed register network. In particular, the system may comprise a plurality of computing application systems that are associated with one another and stored on a distributed register. In this regard, the distributed register may share a code base as well as processing logs for each application. The system may use a deep learning based machine learning process for identifying the dependencies, input data sources for each application, and expected data outputs. Accordingly, if an application experiences a failure, the payload of the failed application may be read and/or processed by another application to maintain the workflow. Furthermore, the system may use AI/ML processes to analyze the code base and/or system logs associated with application failures and generate recommendations for remediating failures.

System for code base sharing during computing application failure using a distributed register network

A system is provided for detecting multi-dimensional linkage and layering of resource transfers using a distributed server network. In particular, the system may comprise a plurality of distributed server nodes that each host a copy of a distributed register, where each of the nodes may be operated by an entity. Each distributed server node may submit, to the distributed register, data records that may contain data regarding potential unauthorized users, accounts, and/or resource transfers. Based on the information within the distributed register, along with various other data inputs, the system may use a deep learning-based graph processing algorithm to identify a multi-dimensional linkage between the users, accounts, and/or resource transfers to extract hidden relationships and potentially unauthorized activity.

System for detecting multi-dimensional linkage and layering of resource transfers using a distributed server network

Systems, methods, and apparatus are provided for integrating access to third-party data using DNA computing. Requests for third-party data may be received from a plurality of applications. The request structure may be tagged with an NFT, linking the request to the originating application. DNA strands may be synthesized from the request structures and clustered based on the encoded attributes. A DNA cluster may be converted to digital data to generate an integrated request structure. Machine learning models may generate an extraction schedule using update information for each third-party vendor. A bot array may apply license credentials to access the vendors and execute an integrated request. An integrated response structure generated from extracted subscription data may be mapped back to the DNA strands. The DNA strands may be decoded to identify the original requests and responses may be transmitted to the originating applications using the NFT linkage associated with the requests.

Optimized subscription access platform using DNA computing

An application monitoring system for identifying and removing unused components from memory associated with an application. Embodiments identify a range, a set of historical logs associated with the range, and components in each historical log. Each component is classified based on a frequency of use of the component, the most recent time the component was used, the number of periods between the most recent use of the component and the range end point, and the number of periods between the most recent use of the component and the previous use of the component. Unused and rarely used components are isolated or removed from the memory.

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Chennai, India

Rajalakshmi Arumugam