Vehicle Telematics Risk Modeling With Dynamic Geographic Baselines
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Solution Overview
Problem
Current systems face limitations in utilizing telematics data from connected vehicles and mobile devices for automated processes, requiring human reconciliation which leads to timeliness issues, inaccuracies, and inconvenience, and fail to generate precise risk models for insurance coverage.
Innovation Solution
A data analytics computing device applies machine-learning techniques to geolocation and telematics data from vehicles to define standard driving behavior within geographic areas, generating refined, location-dependent risk models that can be dynamically updated in real-time, incorporating vehicle characteristics and environmental data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reconciliation is used to process telematics data, then data accuracy may be improved, but timeliness deteriorates and manual effort increases
Solution Approach 1:
The system enables automated self-service processing of telematics data through machine learning models that automatically reconcile and analyze data without human intervention, achieving both high accuracy and timeliness simultaneously
Solution Approach 2:
The patent replaces manual human reconciliation with automated machine learning-based processing systems, substituting mechanical human labor with computational algorithms that process data faster and more consistently
2Adaptability or versatility
If traditional risk models are used, then general coverage is provided, but precision in location-specific risk assessment deteriorates
Solution Approach 1:
The system applies local quality by creating location-specific risk models that tailor risk assessment to geographic characteristics, driving behavior patterns, and environmental factors of specific areas rather than using uniform general models
Solution Approach 2:
The risk models are made dynamic by continuously updating them with real-time telematics data, allowing the systems to adapt to changing driving patterns, traffic conditions, and emerging risk factors in each location
3Device complexity
If static risk models are used, then model simplicity is maintained, but real-time risk assessment capability deteriorates
Solution Approach 1:
The system transitions from static to dynamic risk models that automatically update in real-time as new telematics data becomes available, enabling continuous risk assessment without requiring complex manual model revisions
Solution Approach 2:
The system implements feedback loops where real-time telematics data is continuously fed back into the risk models, allowing automatic adjustment and refinement of risk assessments based on current driving behavior and conditions
Data Source
AI summary
A data analytics computing device for generating refined risk models responsive to vehicle-based telematics data is configured to receive geolocation data and telematics data associated with a plurality of vehicles. The geolocation data is captured at a location sensor of a vehicle computing device and representative of a location of the vehicle, and the telematics data is captured at a plurality of sensors of at least one of the vehicle or the vehicle computing device and representative of movement of the vehicle. The plurality of vehicles is associated with a geographic area. The data analytics computing device is also configured to apply machine-learning techniques to the geolocation data and the telematics data to define standard driving behavior within the geographic area, and generate driving behavior risk models for the geographic area based upon the standard driving behavior.