Usage-Based Predictive Rating for Automated Insurance Underwriting
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Solution Overview
Problem
Insurance companies face challenges in determining insurance prices efficiently due to data quality, availability, and privacy concerns, as well as the need to ensure fair underwriting and rating processes, while traditional methods lack flexibility and require comprehensive human evaluations.
Innovation Solution
Implementing multivariate predictive systems that analyze usage-based data from mobile devices, wearable technology, and home monitoring systems to identify trends and patterns, enabling automated underwriting and real-time insurance pricing based on individual behavior and environmental data, using in-memory data grids and multi-core systems for enhanced processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional underwriting and rating methods are used, then data quality and availability issues are minimized, but the system lacks flexibility and requires comprehensive human evaluations
Solution Approach 1:
The patent replaces manual underwriting processes with automated machine learning models that process multiple data types (usage-based data from mobile devices, wearable technology, home monitoring systems) to generate insurance pricing decisions. This substitution of mechanical human evaluation with automated computational systems provides flexibility while managing complexity through standardized algorithms.
Solution Approach 2:
The system integrates multiple data sources including mobile device usage data, wearable technology data, and home monitoring system data into a unified predictive model. This multi-functional approach allows a single system to process diverse data types for comprehensive underwriting decisions, enhancing adaptability across different insurance scenarios.
2Productivity
If comprehensive human evaluations are conducted, then accuracy of underwriting is improved, but time consumption and cost increase
Solution Approach 1:
The system enables self-service underwriting where automated machine learning models independently process data from multiple sources and generate pricing decisions without requiring comprehensive human evaluation. The model autonomously analyzes usage-based data, wearable technology data, and home monitoring data to produce underwriting recommendations, significantly reducing time consumption while maintaining productivity.
Solution Approach 2:
The system performs preliminary data processing and analysis automatically before human review is needed. Machine learning models pre-process multiple data types, identify patterns, and generate initial underwriting recommendations, allowing human evaluators to focus only on complex cases. This preliminary automated action dramatically reduces the time required for comprehensive evaluations.
3Measurement precision
If multiple data sources are integrated, then predictive accuracy is improved, but data quality and privacy concerns increase
Solution Approach 1:
The patent introduces an intermediary data processing layer that handles multiple data sources (mobile devices, wearables, home monitoring systems) through standardized protocols. This intermediary layer validates, cleans, and normalizes data from various sources before feeding it into predictive models, ensuring data quality while maintaining the benefits of multi-source integration for improved accuracy.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor data quality and model performance. The machine learning models receive feedback from actual insurance outcomes and adjust their predictions accordingly. This feedback loop ensures that accuracy improvements are sustained while allowing for real-time detection and correction of data quality issues across multiple data sources.
4Productivity
If automated underwriting is implemented, then processing speed is improved, but potential for discrimination increases
Solution Approach 1:
The patent transforms traditional underwriting parameters into optimized machine learning models that process multiple data types. The system changes the approach from single-factor traditional rating to multivariate predictive modeling that considers usage behavior, wearable data, and environmental factors. This parameter transformation enables automated underwriting efficiency while reducing discrimination by considering broader, more nuanced risk factors.
Data Source
AI summary
A multivariate predictive system processes usage based data that includes a database engine that provides access to a plurality of database management systems that mine diverse sources of usage based data. A digital filter selectively filters data streams that include usage based data. A sampler automatically divides the filtered data into sample groups and stores the sample groups in a memory. The sampler divides the filtered data according to insurance rating variables processed by a processor programmed to underwrite an insurance product and rate that insurance product. Some samplers store the divided filter data in a local memory. A multiplier multiples rating factors preprogrammed with the insurance rating variables with a base rate of insurance to determine an insured's insurance premium.