Parametric Engine for Machine Learning Weather Risk Analytics
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Solution Overview
Problem
Traditional methods for protecting against future parametric damages, such as weather-related events, are vulnerable to uncertainty and change factors, rendering coverage insufficient or unnecessary, and lack efficiency and accuracy.
Innovation Solution
A parametric engine utilizing machine learning algorithms and distributed ledgers to analyze weather data, determine the likelihood of parametric events, and offer updated coverage through smart contracts, ensuring secure and accurate risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional coverage methods are used to protect against future parametric damages, then coverage is provided, but the coverage becomes insufficient or unnecessary due to uncertainty and change factors
Solution Approach 1:
The system performs preliminary analysis of weather data and parametric events before coverage is needed, using machine learning algorithms to predict future events and update coverage proactively rather than reactively. This allows the system to anticipate changes and adjust coverage before the actual event occurs, preventing coverage insufficiency.
Solution Approach 2:
The system continuously monitors weather data and parametric events, using feedback loops to update coverage based on actual conditions. The machine learning models learn from historical data and real-time inputs, continuously improving coverage accuracy and adapting to uncertainty and change factors.
2Productivity
If conventional techniques are used for coverage determination, then simplicity is maintained, but efficiency and accuracy are reduced
Solution Approach 1:
The system replaces conventional manual or rule-based coverage determination with machine learning algorithms and automated data analysis. This substitution of mechanical/rule-based systems with intelligent algorithms improves efficiency and accuracy while managing complexity through automation.
Solution Approach 2:
The system performs self-analysis of weather data and parametric events using embedded machine learning models, automatically determining coverage requirements without external intervention. This self-service capability improves efficiency by eliminating manual processes while the modular architecture manages complexity.
3Measurement precision
If detailed weather data analysis is performed to improve accuracy, then data accuracy is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system pre-processes and stores weather data in structured formats, performing preliminary analysis before queries are made. Machine learning models are trained in advance on historical data, so when actual coverage determination is needed, the system can quickly retrieve and apply pre-computed insights, reducing processing time while maintaining high accuracy.
Solution Approach 2:
The system applies different levels of analysis to different data elements, focusing computational resources on the most critical weather parameters and parametric events relevant to specific coverage types. This selective detailed analysis maintains accuracy for key factors while reducing overall processing time by not uniformly analyzing all data at maximum depth.
Data Source
AI summary
Systems and methods are described for performing analysis of parametric events. The method may include: (1) receiving weather data from a weather oracle network; (2) calculating, using a first trained machine learning algorithm, a likelihood of a trigger activation for a parametric event (or a trigger event) for a user based at least upon the weather data; (3) calculating, using a second trained machine learning algorithm, an estimated loss for the user based at least upon the likelihood of the trigger activation; (4) determining an initial coverage for the user; and (5) determining whether to offer the user updated coverage for the parametric event based at least upon a comparison of the initial coverage and the estimated loss for the user.