Abstract: Aspects of the disclosure describe methods and systems involving separate, parent (e.g., public) and child (e.g., private) metaverses with varying confidentiality and access privileges for auditor users, financial analyst users, regulatory users, and other virtual reality/metaverse users to interact in a fully immersive, real-time manner (e.g., using an immersive query modulation engine) to fulfill audit and regulatory reporting requirements, including extracting information from heterogeneous data stores, recordkeeping of audit responses for regulatory purposes, and maintaining access privileges of varying user profiles.

Abstract: Aspects of the disclosure relate to a cloud-based parallel processing and cognitive learning computing platform. A computing platform may receive query data comprising a plurality of requested metrics. The computing platform may select a secondary node to process the query request from a plurality of secondary nodes of the computing platform. The computing platform may send the query request to the secondary node. The computing platform may send a request to generate a metric mapping table for a first requested metric to a cognitive learning module of the computing platform. The cognitive learning module may generate the metric mapping table for the first requested metric using neuron clusters that comprise a plurality of unique programmable quadratic function neurons. The computing platform may calculate a value for the first requested metric based on the metric mapping table.

Abstract: Aspects of the disclosure relate to dynamically analyzing data feeds to prioritize and execute actions. A computing platform may receive, from data feed source computer system(s), data feed source information. The computing platform may receive, from a data analyst computing system, risk prioritization information associated with the data feed source information. The computing platform may process, using an artificial intelligence data processing engine, the risk prioritization information to compute a weighted risk prioritization value. The computing platform may generate, using a dynamic analysis output module, based on the weighted risk prioritization value, an output command for the enterprise system(s), and transmit the output command to the enterprise system(s), which may cause the enterprise system(s) to execute at least one action associated with the data feed source information.