Dynamic Insurance Premium Adjustment via Digital Twin Monitoring
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Solution Overview
Problem
Current micro-insurance premium determination methods do not adequately consider the user's expertise, frequency of object operation, and operational conditions, leading to misaligned premium values.
Innovation Solution
A method that generates a baseline insurance coverage policy for objects operated by users, utilizing reinforcement learning and digital twin technology to simulate various scenarios, and adjusts premium values based on real-time monitoring data, incorporating user-specific profiles and learning programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional micro-insurance premium determination methods are used, then premium values can be calculated based on object health predictions, but the premium values do not reflect user expertise, operation frequency, or operational conditions
Solution Approach 1:
The patent implements dynamic premium adjustment by continuously monitoring user operation data, object status, and risk factors in real-time. The premium value is not fixed but dynamically updated based on changing operational conditions, user expertise level, and object health status, allowing the system to adapt to varying risk profiles throughout the insurance period
Solution Approach 2:
The system changes multiple parameters simultaneously to determine premium values: user expertise parameters (through learning programs and experience tracking), operational parameters (frequency, duration, conditions), and object parameters (health predictions, digital twin data). By adjusting these parameters dynamically, the system achieves precise premium valuation that reflects actual risk
2Adaptability or versatility
If baseline insurance coverage policies are generated without real-time monitoring, then initial premium values can be set, but the premiums remain misaligned with actual user behavior and risk profiles
Solution Approach 1:
The patent implements continuous feedback loops where user operation data, object sensor data, and risk monitoring information are constantly collected and fed back into the premium calculation system. This real-time feedback enables immediate premium adjustments that reflect current risk levels, eliminating delays between risk changes and premium updates
Solution Approach 2:
The system maintains continuous monitoring and continuous premium adjustment throughout the insurance coverage period. Rather than periodic updates, the useful action of risk assessment and premium calculation operates continuously, ensuring premium values always align with current risk profiles without interruption or delay
3Measurement precision
If user-specific data collection and monitoring are implemented, then personalized premium values can be achieved, but system complexity increases
Solution Approach 1:
The patent employs a multi-functional agent system where a single AI agent performs multiple functions: collecting user data, monitoring object status, predicting health, calculating premiums, and providing recommendations. This universal agent architecture reduces overall system complexity compared to having separate dedicated systems for each function while maintaining precise user-specific premium calculation
Solution Approach 2:
The digital twin serves as an intermediary that simplifies complex monitoring by creating a virtual representation of the physical object. Instead of directly monitoring numerous physical parameters, the system interacts with the digital twin which processes and synthesizes sensor data, reducing the complexity of data collection and analysis while enabling precise risk assessment
4Productivity
If reinforcement learning and digital twin technology are used, then dynamic premium adjustment is enabled, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial reinforcement learning by focusing computational resources on the most critical risk factors and parameters that have the greatest impact on premium values. Rather than continuously retraining models on all available data, the system selectively updates based on significant changes in user behavior or object status, reducing computational overhead while maintaining accurate premium adjustment
Solution Approach 2:
The digital twin creates a computational copy of the physical object that can be simulated and analyzed without consuming additional physical resources. By performing risk assessments and scenario simulations on the digital copy rather than requiring physical testing or monitoring, the system reduces energy consumption while enabling rapid premium adjustments based on virtual experimentation and prediction
Data Source
AI summary
A method, computer system, and a computer program product for insurance premium determinations is provided. The present invention may include generating a baseline insurance coverage policy for an object. The present invention may include presenting a user with the baseline insurance coverage policy prior to an operation of the object. The present invention may include monitoring the operation of the object by the user.