Wells Fargo Patent for Computing Tool Risk Discovery

ChangeBridge: Patent Grants - Business Methods (G06Q)

Published March 24th, 2026

Detected March 25th, 2026

Summary

The USPTO has granted Wells Fargo Bank, N.A. a patent for a computing tool risk discovery system. The patent, titled 'Computing tool risk discovery,' utilizes machine learning to automatically identify and classify risks associated with end-user computing tools, enabling mitigation actions.

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What changed

The United States Patent and Trademark Office (USPTO) has granted patent US12586087B2 to Wells Fargo Bank, N.A. for a method of automatically discovering risks associated with end-user computing tools. The patent leverages machine learning models to classify these tools based on financial or reputational risk and extent (high or low), and also allows for automatic clustering and risk assessment. Mitigation actions can then be performed to reduce identified risks.

This patent grant is primarily an intellectual property development for Wells Fargo and does not impose new regulatory obligations on other entities. However, it highlights a technological advancement in cybersecurity and risk management within the financial sector. Companies in the financial services industry, particularly those utilizing advanced analytics and machine learning for risk assessment, may find this patent relevant to their own technology development and intellectual property strategies.

Source document (simplified)

← USPTO Patent Grants

Computing tool risk discovery

Grant US12586087B2 Kind: B2 Mar 24, 2026

Assignee

Wells Fargo Bank, N.A.

Inventors

Robert Lee Posert

Abstract

Risk associated with end-user computing tools can be discovered automatically. Machine learning and other approaches can be employed to automate discovery of risk associated with end-user computing tools. In one instance, a machine learning model can be constructed and fine-tuned through training that can classify end-user computing tools in terms of risk. The risk can be of a particular type, such as financial or reputational risk, and extent, such as high or low. In another instance, end-user computing tools can be subject to automatic clustering and subsequent risk assessment. Mitigation action can be performed to reduce risk associated with high-risk end-user computing tools.