Vehicle Data Analysis for Fair Usage-Based Insurance Pricing
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Solution Overview
Problem
Usage-based insurance (UBI) systems face challenges in accurately assessing driver risk due to reliance on proxy variables, leading to increased premium variance and potential discrimination issues, particularly affecting young drivers and regions with regulations against certain auto rating variables.
Innovation Solution
A system and method that utilize image processing and deep learning to analyze vehicle data, such as location and speed measurements, to identify driving patterns and assess risk, providing a more accurate and fair pricing scheme by focusing on long-term driving habits rather than individual data points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional auto rating variables (age, gender, territory, marital status) are used in UBI models, then premium calculation is simplified, but discrimination issues and regulatory compliance problems arise
Solution Approach 1:
The patent extracts and removes problematic rating variables (age, gender, territory, marital status) from the UBI model while retaining the core functionality of risk assessment through alternative causal variables that do not trigger discrimination concerns
Solution Approach 2:
The patent changes the parameter set used for risk assessment from demographic proxies to causal driving behavior variables, fundamentally altering the basis of premium calculation while maintaining the system's ability to differentiate risk levels
2Ease of operation
If proxy variables are used instead of causal variables in UBI models, then data collection is easier, but measurement precision and fairness deteriorate
Solution Approach 1:
The patent substitutes traditional proxy-based rating mechanisms with a causal modeling approach that uses telematics data to directly measure driving behavior, replacing indirect demographic indicators with direct observations of actual driving patterns
3Speed
If individual data points are used for risk assessment, then real-time responsiveness is improved, but measurement precision deteriorates due to lack of context
Solution Approach 1:
The patent performs preliminary actions by collecting and storing contextual information about driving environments and conditions in advance, which is then used to interpret individual data points accurately when risk assessment is needed
Solution Approach 2:
The patent introduces contextual information as an intermediary that mediates between individual data points and risk assessment outcomes, providing the necessary context to accurately interpret raw data while maintaining responsive assessment capabilities
Data Source
AI summary
A system and method are provided for analyzing vehicle data. The method is executed by a device having a processor and includes obtaining a set of vehicle data via a data interface, the set of vehicle data comprising a plurality of location measurements and a corresponding plurality of speed measurements for a vehicle. The method also includes associating the plurality of location measurements and the plurality of speed measurements with a geographic area to generate a geographic map image. The method also includes applying an image processing technique to the map image to identify at least one path and analyzing the at least one path and data associated with the geographic area, to identify at least one driving pattern within the geographic area. The method also includes providing an indication of the at least one driving pattern to contribute to a risk assessment associated with a driver of the vehicle.