Patent ID: 11922352
Assignee: UNITED SERVICES AUTOMOBILE ASSOCIATION (USAA)
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A tangible, non-transitory, computer-readable medium, comprising computer-readable instructions that, when executed by one or more processors of a computer, cause the computer to:
receive first risk identification information from a first risk silo associated with a first subsystem of a system;
receive second risk identification information from a second risk silo associated with a second subsystem of the system;
store the first risk identification information and the second risk identification information in a risk aggregation data store;
receive a change request indicating a potential change to the system, wherein the change request comprises a specialized computer parsable file comprising a tag structure, wherein the computer parsable file comprises tags that define conditions and corresponding computations associated with the system in response to the potential change;
identify key words, phrases, or both from the change request by performing machine learning based upon the tags using a machine learning algorithm comprising a mathematical model configured to learn an objective function, wherein the mathematical model comprises input data and output data, wherein the objective function is configured to enable the machine learning algorithm to determine the output data for the input data that is not a part of training data used to train the mathematical model;
identify machine learning patterns indicating the potential change by performing the machine learning based upon the key words, phrases, or both;
identify a risk associated with the change request, by performing the machine learning based upon the machine learning patterns, using the first risk identification information and the second risk identification information as the training data;
present the risk via an electronic risk report;
add the risk to the risk aggregation data store configured to update the training data based on the risk;
iteratively optimize the objective function based on the updated training data;
retrain the mathematical model to learn the iteratively optimized objective function based on the updated training data;
enable the first risk silo and the second risk silo to cross-identify and report one or more new risks across the first subsystem and the second subsystem with improved accuracy based on the first risk identification information and the second risk identification information using the machine learning comprising the iteratively optimized objective function and retrained mathematical model;
receive an electronic document from the system;
generate recommendations based on a risk compliance analysis using the machine learning based on the key words, phrases, or both, and the electronic document; and
generate a graphical markup of the electronic document based on the recommendations, wherein the electronic markup comprises an interactive graphic user interface configured to allow a user to modify the electronic document based on the recommendations.