Bank of America Corporation

Vinod Maghnani

Saisrikanth Chitty

Mukesh Katare

Sandeep Kumar Chauhan

Shailendra Singh

Gaurav Bansal

Shekhar Singh Mehra

Tulasi Bhavani Nekkanti

Nikhil Pathak

Amit Mehndiratta

Ashish Kumar Dwivedi

Sarika Agarwal

Systems, computer program products, and methods are described herein for authentication of device transfers in a distributed network. The present disclosure is configured to receive a transaction request, wherein the transaction request comprises a secondary user device, wherein the secondary user device is associated a user; create a pair of authentication codes, wherein the authentication codes are associated with a distributed network; transmit the pair of authentication codes to the secondary user device; authorize the secondary user device; activate a primary user device, wherein the primary user device is associated with the user, and wherein activating the primary user device comprises prompting the user on the primary user device to provide one or more transaction details; and complete the transaction request.

System and method for authentication of device transfers in a distributed network

Aspects of the disclosure relate to a dynamic caching platform. The dynamic caching platform may train a machine learning model based on historical complexity score information. The dynamic caching platform may receive information streams from a client metaverse device and a metaverse host system. The dynamic caching platform may generate a complexity score based on the interaction information streams using the machine learning model. The dynamic caching platform may compare the complexity score to complexity thresholds. Based on the comparison, the dynamic caching platform may identify caching rules. The dynamic caching platform may cache interaction information based on the caching rules. The dynamic caching platform may update the complexity score using the machine learning model. The dynamic caching platform may update the caching rules based on the updated complexity score. The dynamic caching platform may cache interaction information based on the updated caching rules.

Real-time dynamic caching platform for metaverse environments using non-fungible tokens

Systems, methods, and apparatus are provided for fraud screening via smart glasses interactions during an ATM session. A smart glasses device may capture an image of an ATM environment. The ATM and the smart glasses device may be edge nodes on an edge network. An edge platform may use a fraud detection model to classify the image and compare it to stored ATM images. The model may be trained at an enterprise server and stored on the edge platform. In response to a determination of fraud at the edge platform, a fraud alert may be transmitted to the smart glasses device during the ATM session. Edge computing reduces latency to enable real-time smart glasses alerts. The smart glasses device may communicate the fraud alert to other smart glasses devices on the edge network.

Smart glasses based detection of ATM fraud

Embodiments of the invention are directed to systems, methods, and computer program products for dynamic processing of temporal upstream data and downstream data in communication networks. The invention is configured for dynamic processing and cascading of instance data and configuration files from edge node devices of a distributed network for reduction of latency in data transmissions. The invention involves constructing a first device cluster comprising one or more first cluster edge computing nodes of a plurality of edge computing nodes. In response to receiving a first downstream request for determining a current modification to the first source instance data, embodiments of the invention involves extracting at least one cluster configuration file associated with the at least one cluster edge computing node associated with first edge computing node, and processing the first technology application therewith.

Electronic system for dynamic processing of temporal upstream data and downstream data in communication networks

Aspects of the disclosure relate to real-time knowledge-based widget prioritization and display. A computing platform may detect, via a computing device, a voice-based interaction between an enterprise agent and a customer. Then, the computing platform may cause, via the computing device, the voice-based interaction to be captured as audio data. The computing platform may then transform the audio data to textual data. Subsequently, the computing platform may identify, in the textual data, a customer query. Then, the computing platform may retrieve, in real-time and based on the voice-based interaction and from a repository of widgets, a first widget, where the first widget includes information at least partially responsive to the customer query. Then, the computing platform may display, to the enterprise agent and via a graphical user interface in use by the enterprise agent, the first widget.

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Haryana, India

Vinod Maghnani