ML Risk Classification for End-User Computing Tools
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
End-user computing tools, such as spreadsheets, pose significant risks to organizations due to unintentional errors, fraud, and data breaches, and manual review is impractical and inefficient for large organizations.
Innovation Solution
A machine learning model is trained to automatically classify and mitigate risks associated with end-user computing tools by employing supervised learning, unsupervised learning, and context information, identifying risks such as financial, reputational, and regulatory, and applying mitigation actions like review, tracking, or monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of end-user computing tools is performed, then risk assessment accuracy is improved, but productivity and scalability deteriorate
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning-based analysis system. The ML model automatically analyzes end-user computing tools, formulas, and data relationships to assess risks, substituting human reviewers with an algorithmic system that maintains assessment quality while dramatically increasing throughput and scalability.
Solution Approach 2:
The system enables end-user computing tools to be self-assessed through automated ML analysis. The machine learning model independently evaluates tools for risks such as errors, fraud, and data breaches without requiring external manual review, allowing the organization to scale risk assessment across numerous tools simultaneously.
2Difficulty of detecting and measuring
If manual review processes are implemented, then risk detection capability is improved, but loss of time and operational efficiency worsen
Solution Approach 1:
The patent implements preliminary automated risk assessment using machine learning models that continuously analyze end-user computing tools before issues arise. The system proactively identifies potential errors, fraud risks, and data breach vulnerabilities in advance, eliminating the need for time-consuming reactive manual reviews after problems occur.
Solution Approach 2:
The system replaces time-intensive manual risk detection with automated machine learning analysis that processes end-user computing tools rapidly. The ML model can evaluate numerous tools simultaneously, detecting risks in fractions of the time required for human review while maintaining or improving detection capability.
3Reliability
If comprehensive quality assurance processes are applied to end-user computing tools, then reliability is improved, but device complexity and implementation difficulty worsen
Solution Approach 1:
The patent replaces complex manual quality assurance processes with an automated machine learning system. The ML model handles the complexity of analyzing formulas, data relationships, and potential risks internally, presenting users with simple risk assessments and recommendations. This maintains high reliability for decision-making while reducing the perceived complexity for end users.
Solution Approach 2:
The machine learning model acts as an intermediary between end-user computing tools and quality assurance processes. It translates complex tool structures and data relationships into simplified risk assessments, bridging the gap between sophisticated analysis needs and user-friendly implementation.
Data Source
AI summary
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.