Simulated Consumer Profiles for Insurance Pricing Calibration
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Solution Overview
Problem
Existing insurance comparison software applications fail to adequately simplify complex insurance product information into actionable, low-dimensional outputs for consumers, struggling with the 'curse of dimensionality' and opaque pricing mechanisms, making it difficult for consumers to understand their insurance options and pricing factors.
Innovation Solution
A process involving simulated consumer profiles is used to calibrate models, obtaining access to a pricing analytics application, determining sub-regions of the input parameter space, forming and querying simulated profiles, and associating insurance prices to select representative profiles, which helps in defining specifications of constrained variation and forming additional simulated profiles to refine insurance pricing records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing insurance comparison software applications evaluate various scenarios by navigating through several dimensions related to insurance products, then they can provide comprehensive product information, but they fail to provide a sufficiently low dimensional characterization that is actionable and relevant to consumers
Solution Approach 1:
The patent extracts only the most relevant and actionable dimensions from the complex multi-dimensional insurance product space. By identifying and extracting key factors that truly impact consumer decisions, the system reduces information overload while maintaining essential product differentiation capabilities.
Solution Approach 2:
The patent transforms the high-dimensional insurance product space into a lower-dimensional representation by introducing new aggregation dimensions. Instead of presenting consumers with numerous individual product attributes, the system creates synthesized dimensions that capture essential variations in a more manageable format.
2Measurement precision
If insurance pricing models consider 10 or more different attributes of the consumer to determine policy price, then they can achieve accurate pricing, but they create complexity that is opaque and confusing for consumers
Solution Approach 1:
The patent segments the complex pricing model into distinct, interpretable components. By dividing the pricing determination process into separate modules that each handle specific attribute groups, the system maintains pricing accuracy while making the underlying logic more transparent and easier to understand for consumers.
Solution Approach 2:
The patent introduces intermediary representations that bridge the gap between complex multi-attribute pricing models and consumer understanding. These intermediaries translate intricate pricing calculations into simplified explanations that reveal how different consumer attributes influence final prices without exposing the full computational complexity.
3Adaptability or versatility
If pricing analytics applications output insurance prices responsive to more than 150 million different variations in consumer profiles, then they can provide comprehensive pricing coverage, but they struggle with the curse of dimensionality and scale poorly
Solution Approach 1:
The patent applies partial action by focusing computational resources on evaluating only the most significant subsets of consumer profile variations. Instead of exhaustively processing all 150 million possible variations, the system identifies and prioritizes the subset that contributes most to meaningful price differentiation, achieving adequate coverage with improved efficiency.
Solution Approach 2:
The patent changes the parameters of the pricing analytics application by transforming the high-dimensional consumer profile space into a lower-dimensional representation. This parameter transformation reduces the computational burden from 150 million variations to a manageable subset while preserving the essential pricing distinctions needed for accurate quotes.
Data Source
AI summary
Provided is a process of using simulated consumer profiles to construction calibration data from a pricing analytics application having a relatively high dimensional input parameter space.