18608001. COMPUTING TOOL RISK DISCOVERY simplified abstract (Wells Fargo Bank, N.A.)
COMPUTING TOOL RISK DISCOVERY
Organization Name
Inventor(s)
Robert Lee Posert of San Diego CA (US)
COMPUTING TOOL RISK DISCOVERY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18608001 titled 'COMPUTING TOOL RISK DISCOVERY
Simplified Explanation: Automated discovery of risk associated with end-user computing tools using machine learning and other approaches.
Key Features and Innovation:
- Machine learning model for classifying end-user computing tools in terms of risk.
- Automatic clustering and risk assessment of end-user computing tools.
- Mitigation actions to reduce risk associated with high-risk tools.
Potential Applications: This technology can be applied in various industries such as finance, healthcare, and technology to assess and mitigate risks associated with end-user computing tools.
Problems Solved: This technology addresses the challenge of manually identifying and assessing risks associated with end-user computing tools, which can be time-consuming and prone to human error.
Benefits:
- Improved efficiency in identifying and mitigating risks.
- Enhanced security and compliance measures.
- Reduction in potential financial and reputational losses.
Commercial Applications: The technology can be utilized by IT security firms, financial institutions, and regulatory bodies to automate risk assessment processes for end-user computing tools, leading to better risk management and compliance.
Prior Art: Readers can explore existing patents related to machine learning in risk assessment and automated clustering to gain a deeper understanding of the prior art in this field.
Frequently Updated Research: Stay updated on the latest advancements in machine learning algorithms for risk assessment and clustering techniques to enhance the effectiveness of this technology.
Questions about Risk Assessment with Machine Learning: 1. How does machine learning improve the accuracy and efficiency of risk assessment for end-user computing tools? 2. What are the key challenges in implementing automated risk assessment using machine learning in real-world scenarios?
Original Abstract Submitted
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.
