Dynamic Risk Assessment System for Event-Driven Governance
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Solution Overview
Problem
Existing computational techniques fail to effectively manage risk as a continuous variable in complex systems, leading to sub-optimal outcomes due to static risk assessments and inability to update likelihoods of probabilistic events in real-time, particularly in industrial and financial processes.
Innovation Solution
A system that uses machine learning to dynamically assess and manage risk by processing interaction-event records, assigning event-risk scores based on both past and future events, and continuously updating risk models to predict future risks and optimize decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static risk assessment methods are used, then system complexity is reduced, but predictive accuracy and real-time risk management capability deteriorate
Solution Approach 1:
The patent implements dynamic risk assessment by continuously updating risk scores as new events occur in the time series data. The system transitions from static to dynamic evaluation, where risk assessments are recalculated in real-time based on incoming events, allowing the system to adapt to changing conditions while maintaining manageable complexity through automated processes.
Solution Approach 2:
The system incorporates feedback loops where risk assessment outcomes influence subsequent monitoring and control actions. The continuous stochastic process controller uses the calculated risk scores to adjust control parameters, creating a closed-loop system that improves predictive accuracy through iterative refinement while the automation reduces the perceived complexity for operators.
2Reliability
If real-time dynamic risk assessment is implemented, then predictive accuracy and risk management capability improve, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-defining event types, risk models, and control strategies before real-time operation. The continuous stochastic process controller is pre-configured with the mathematical models and parameters needed for risk assessment, allowing it to process incoming events efficiently without requiring complex real-time calculations for every decision, thus reducing computational burden while maintaining reliability.
Solution Approach 2:
The patent changes parameters by transitioning from discrete, periodic risk assessments to continuous real-time evaluation. The system adjusts the frequency and granularity of risk calculations based on the stochastic nature of the process, computing risk scores at intervals optimized for both reliability and computational efficiency, thereby balancing risk management capability with resource consumption.
3Measurement precision
If continuous stochastic process monitoring is performed, then risk detection capability improves, but data processing complexity and storage requirements increase
Solution Approach 1:
The system extracts only the essential features and events from the continuous time series data that are relevant to risk assessment. The continuous stochastic process controller identifies and extracts key events that indicate changes in process state or risk conditions, filtering out redundant information. This extraction approach improves risk detection capability by focusing on critical data points while reducing the overall data processing complexity and storage requirements.
Data Source
AI summary
Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.