Vehicle Insurance Risk Prediction Algorithm Using Personal Attributes
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Solution Overview
Problem
Current vehicle insurance risk assessment methods rely primarily on vehicle attribute information, neglecting personal attributes that significantly influence accident involvement and claim amounts, leading to inaccurate and unreliable risk evaluations.
Innovation Solution
A method and apparatus for predicting vehicle insurance risk by processing personal attribute information, including natural, social, and behavioral data using pre-constructed algorithms, to provide a comprehensive and accurate risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicle insurance risk assessment relies primarily on vehicle attribute information, then the assessment process is simple, but the accuracy and reliability of risk evaluation deteriorates
Solution Approach 1:
The patent merges vehicle attribute information with personal attribute information (including natural attributes, social attributes, and behavioral data) to form a comprehensive risk assessment model. This combination allows the system to maintain relatively simple processing while significantly improving risk evaluation accuracy by considering multiple information dimensions simultaneously.
Solution Approach 2:
The risk assessment system is designed to handle multiple types of attribute information (vehicle attributes, natural personal attributes, social attributes, and behavioral data) through a unified processing framework. This multi-functional approach enables the same assessment mechanism to process diverse data types, improving accuracy without proportionally increasing system complexity.
2Ease of operation
If only vehicle attribute information is used for risk assessment, then data processing is straightforward, but the comprehensiveness of risk prediction deteriorates
Solution Approach 1:
The patent segments personal attribute information into distinct categories: natural attributes (age, gender), social attributes (occupation, education), and behavioral data (driving habits, credit history). This segmentation allows the system to process each type of data appropriately while maintaining overall comprehensiveness, balancing simplicity with thoroughness in risk assessment.
3Measurement precision
If personal attribute information is incorporated into risk assessment, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system performs preliminary processing and categorization of personal attribute information before main risk assessment computation. By pre-organizing data into natural attributes, social attributes, and behavioral data categories, the system reduces the complexity of subsequent processing steps while maintaining high prediction accuracy through comprehensive data utilization.
Data Source
AI summary
A plurality of variable data of personal attribute information associated with at least one vehicle insurance user is received at a prediction server. Based on a service scenario requirement, a pre-constructed prediction algorithm is selected. The plurality of variable data is processed by one or more processors using the pre-constructed prediction algorithm. At least one prediction result is generated as the prediction server.