Loss Propagation Estimation Server for Vehicle Risk Assessment
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Solution Overview
Problem
Current insurance risk analysis systems face challenges in accurately estimating loss propensity of insured vehicles while protecting the privacy of driving location information, as insurers need accurate risk data but the public is hesitant to share location data with third parties.
Innovation Solution
A system and method utilizing telematics devices to estimate loss propensity by processing vehicle data from telematics devices, calculating a numeric loss cost factor based on traveled loss cost areas, and transmitting this factor to insurers for policy underwriting or pricing, while ensuring driving location and route privacy through the use of non-contiguous geographic loss cost areas and a nationwide identification scheme.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If telematics devices are used to obtain driving location and route data from vehicles, then accurate risk information for insurance pricing can be obtained, but driver privacy and location confidentiality are compromised
Solution Approach 1:
The patent introduces an intermediary processing layer between the telematics device and the insurer. The server receives raw location data from the telematics device, processes it through a database that maps locations to loss cost areas, and transmits only aggregated risk information to the insurer. This intermediary layer preserves privacy by not sharing specific location data while still enabling accurate risk assessment through the loss cost area aggregation.
Solution Approach 2:
The patent extracts only the necessary risk information from the complete location data while leaving the specific location details private. By extracting and transmitting only the loss cost area identifiers and aggregated statistics rather than raw location coordinates and routes, the system enables insurers to obtain accurate risk information without accessing sensitive personal location data.
2Reliability
If detailed driving location and route data are shared with insurers, then comprehensive risk assessment is possible, but public trust and data sharing willingness decrease
Solution Approach 1:
The patent changes the parameters of data transmission from detailed location coordinates and routes to aggregated loss cost area identifiers and statistical summaries. This parameter transformation maintains the essential risk assessment information while removing personally identifiable location data, thereby improving data sharing acceptance without compromising risk assessment accuracy.
Solution Approach 2:
The patent segments the geographic space into loss cost areas, which are aggregated regions with associated risk parameters. Instead of sharing detailed location data point by point, the system transmits segmented risk information at the loss cost area level, providing comprehensive risk assessment while protecting individual location privacy and increasing public trust.
3Quantity of substance
If raw telematics data is transmitted directly to insurers, then complete driving information is available, but data processing complexity and security requirements increase
Solution Approach 1:
The patent performs preliminary processing of the telematics data at the server level before transmission to the insurer. The system pre-aggregates location data into loss cost areas, pre-calculates risk statistics, and pre-formats the information for insurance purposes. This preliminary action reduces the complexity of data processing at the insurer end while maintaining information completeness.
Solution Approach 2:
The server acts as an intermediary that simplifies the data structure before transmission. By processing and transforming the raw telematics data into structured loss cost area information and aggregated statistics, the intermediary layer reduces the complexity of data handling for the insurer while preserving the completeness of the risk information needed for underwriting and pricing.
Data Source
AI summary
A system and method for estimating loss propensity of a vehicle and providing driving information are provided. A loss propensity estimation server receives information from a telematics device installed in a vehicle, determines at least one loss cost area through which vehicle has traveled, and calculates a numeric loss cost factor based upon the at least one loss cost area relative to the amount of risk indicated by the vehicle's garaging loss cost. The numeric loss cost factor can be transmitted to an insurer for subsequent use by the insurer in underwriting or pricing a future insurance policy. A driving information database in the loss propensity estimation server stores driving information obtained from the telematics device installed in the vehicle, which can subsequently be transmitted to an insurer.