Territory Factor Segmentation for Insurance Pricing Accuracy
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Solution Overview
Problem
Current rating and pricing systems for business insurance policies lack specificity and flexibility, failing to adequately consider territorial variations in risk, leading to inconsistent and inaccurate pricing across different geographical locations.
Innovation Solution
An automated insurance processing platform that calculates territory factors by analyzing historical loss data, geographical, and demographic data to generate territory sets and factors, which are then used to improve the rating and pricing of business insurance policies, incorporating these factors into the premium calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current rating and pricing systems are used, then the pricing process is simple, but the pricing specificity and flexibility are insufficient and pricing is inaccurate across different geographical locations
Solution Approach 1:
The patent segments the rating system into multiple independent components: territory factors, coverage type factors, and risk characteristic factors. Each component can be calculated and applied separately, allowing for precise pricing adjustments for different geographical locations without overwhelming complexity in the overall system.
Solution Approach 2:
The patent implements local quality by calculating territory-specific factors that capture the unique risk characteristics of different geographical areas. Each territory is assigned factors based on local loss history, demographics, and environmental conditions, enabling accurate pricing for each location while maintaining a unified rating framework.
2Adaptability or versatility
If territory factors are calculated by analyzing multiple data types, then pricing specificity improves, but the calculation time and processing complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing territory factors, coverage type factors, and risk characteristic factors in a database before the actual pricing process. When a quote is needed, the system retrieves pre-computed factors rather than calculating them in real-time, significantly reducing quote generation time while maintaining pricing flexibility.
Solution Approach 2:
The patent uses copying by creating a standardized rating framework that can be replicated across different territories and coverage types. The same core algorithm and factor structure are applied universally, allowing for flexible pricing adjustments without requiring complex ad-hoc calculations for each specific case.
3Measurement precision
If detailed analysis of historical loss data and demographic data is performed, then rating precision improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts the essential information from complex historical loss data and demographic data by identifying and isolating key risk characteristics such as loss frequency, loss severity, population density, and crime rates. These extracted factors are then used to calculate territory and coverage type factors, simplifying the data processing while maintaining rating precision.
Solution Approach 2:
The patent applies parameter changes by transforming raw historical data into standardized rating factors and parameters. Historical loss data is converted into loss frequency and severity parameters, and demographic data is transformed into risk characteristic parameters. This standardization enables precise rating while reducing data processing complexity through consistent parameter transformation.
Data Source
AI summary
Systems, methods, apparatus, computer program code and means for rating and pricing insurance policies are provided. In some embodiments, an automated insurance processing platform rates and prices insurance policies by including a territory factor in the calculation of a premium for the policy. Pursuant to some embodiments, the territory factor is calculated by receiving historical loss data, geographical data, and demographic data, analyzing the historical loss data, the geographical data, and the demographic data to identify data having similar claim behaviors. The historical loss data is analyzed to identify at least a frequency and severity of historical loss by coverage type. The frequency and severity of loss data, the geographical data, and the demographic data is iteratively analyzed to create a territory set having different geographical boundaries for the different coverage types; and the territory set is used to generate a set of territory factors for the different coverage types and the territories.