AI Risk Management Digital Twin for Proactive Damage Assessment
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Solution Overview
Problem
Current risk management systems are inefficient and prone to errors due to complex and cumbersome processes, requiring skilled labor, and often focus on reactive approaches, failing to proactively prevent damages and optimize resource utilization.
Innovation Solution
An AI and IoT-based risk management system that uses intelligent agents for proactive damage prevention, efficient claim adjudication, and resource optimization, incorporating data analyzers and risk control agents to normalize and analyze sensor data, and implement machine learning and knowledge discovery techniques for accurate damage assessment and policy formulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If skilled workers are used for damage assessment and risk management, then accuracy and knowledge-based decision making improve, but cost and time consumption increase
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the physical resource that replicates its characteristics, behavior, and state. This digital replica can be analyzed computationally to assess damage and predict failures without requiring physical inspection by skilled workers, thereby maintaining accuracy while reducing time consumption.
Solution Approach 2:
The patent replaces manual mechanical inspection processes with computational analysis of sensor data and digital twin simulations. Machine learning algorithms and physics-based models substitute for human expert analysis, enabling automated damage assessment that is both accurate and time-efficient.
2Reliability
If manual analysis and skilled workers are deployed, then knowledge and expertise are utilized, but resource intensity and cost increase
Solution Approach 1:
The digital twin system enables the resource itself to provide data about its own state through integrated sensors and monitoring systems. The system performs self-diagnosis and self-assessment by comparing actual sensor data against the digital twin's expected behavior, eliminating the need for external skilled workers while maintaining reliable decision-making.
Solution Approach 2:
The digital twin acts as an intermediary between the physical resource and the analysis system. It translates complex physical phenomena into computable virtual representations, allowing automated algorithms to analyze resource state without requiring direct human expertise in the underlying physical domains.
3Productivity
If reactive claim adjudication is performed, then compensation is provided after damage, but proactive prevention capabilities are lost
Solution Approach 1:
The digital twin continuously simulates and predicts future states of the resource, identifying potential failures and damage scenarios before they occur in the physical system. This enables proactive prevention measures to be taken based on predicted issues, transforming the system from reactive to proactive while maintaining efficient processing through automated predictions.
4Productivity
If automated systems are implemented, then speed and efficiency improve, but ability to replicate human knowledge and judgment decreases
Solution Approach 1:
The patent transforms qualitative expert knowledge and judgment criteria into quantifiable parameters and computational models. By converting human expertise into mathematical relationships and physics-based models within the digital twin, the system enables automated algorithms to replicate human judgment accuracy while maintaining high processing speeds.
Data Source
AI summary
Examples of artificial intelligence based risk and knowledge management analysis are described. In an example implementation, a data analyzer may obtain entity data pertaining to an entity associated with a risk management instrument. The entity data may include data obtained from an IoT device and/or a risk control and knowledge management database. The entity data may be processed by an intelligent risk management agent to perform a variety of risk control and knowledge management tasks, such as claim processing, notification generation, formulization of risk management instruments, and assisting agents, users, and organization. The claim processing may include, for instance, identification of a similar case from database. The notification generation may include analysis with respect to reference parameters. The formulization of risk management instrument may include analysis with respect to the entity data from multiple domains and/or various external factors.