Dynamic Sensor Data Weighting for Property Risk Assessment
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Solution Overview
Problem
Existing systems for utilizing sensor data in insurance pricing and underwriting fail to accurately account for the complex relationships between customer characteristics, property characteristics, and sensor data, leading to ineffective commercialization of insurance products that incorporate remote property monitoring.
Innovation Solution
A system that processes sensor data using statistical models, neural networks, or decision trees, dynamically selecting and weighting data parameters based on property and customer characteristics to make informed underwriting and pricing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is collected and processed using uniform methods for all customers and properties, then the system is simple to operate, but the accuracy of risk assessment and pricing decisions deteriorates due to failure to account for complex relationships between customer characteristics, property characteristics, and sensor data
Solution Approach 1:
The patent segments the uniform data processing approach into customized processing paths based on customer characteristics (e.g., age, driving history) and property characteristics (e.g., location, type). Different sensor data parameters are selected and weighted according to specific customer-property segments, enabling accurate risk assessment while managing complexity through structured segmentation.
Solution Approach 2:
The system dynamically adjusts the selection and weighting of sensor data parameters based on the specific characteristics of each customer and property combination. Rather than using a static uniform processing method, the system adapts its data analysis approach in real-time to match the relevant risk factors for each insured entity, improving measurement precision without requiring overly complex fixed infrastructure.
2Productivity
If all sensor data parameters are processed with equal weight for every customer and property, then the processing method is simple and consistent, but the effectiveness of insurance pricing and underwriting decisions deteriorates due to inability to account for varying relevance of different data parameters
Solution Approach 1:
The patent applies local quality by assigning different weights to different sensor data parameters based on their specific relevance to each customer-property combination. For example, certain parameters may be weighted more heavily for young drivers while other parameters receive more weight for properties in high-risk locations. This localized weighting approach maximizes the effectiveness of insurance decisions by focusing analytical resources on the most relevant risk indicators for each case.
3Loss of information
If the system collects and analyzes extensive sensor data for every customer and property, then the completeness of risk information is improved, but the loss of time for data processing and decision-making increases
Solution Approach 1:
The patent extracts and selects only the most relevant sensor data parameters for processing based on customer characteristics and property characteristics. Rather than analyzing all available sensor data uniformly, the system identifies and extracts the specific subset of parameters most relevant to each risk assessment, maintaining information completeness while significantly reducing processing time through targeted data extraction.
Data Source
AI summary
The invention relates to systems and methods for analyzing data collected from sensors monitoring property. In particular, the systems and methods analyze the data to make property insurance underwriting decisions based on the collected sensor data using a computerized process that varies the way in which it manipulates the collected sensor data based on a characteristic of the property being insured, a characteristic of the entity seeking the insurance, and/or on the value of one or more collected data parameters. The invention also relates to systems and methods of making property insurance pricing decisions based on a similarly dynamic computerized process.