Liability Risk Drivers for Adaptive Insurance Pricing
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Solution Overview
Problem
Current insurance systems face challenges in accurately pricing insurance products due to uncertainties in future loss predictions, particularly for liability risks, as historical data often fails to account for region-dependent and subgroup-specific risk factors, leading to inefficient risk management and potential financial losses.
Innovation Solution
A liability risk-driven system that uses independently operated liability risk drivers and a control unit controller to dynamically measure and adapt to liability exposure signals, enabling automated optimization and adaptive pricing by segmenting risk factors and utilizing granular statistical data to refine loss assumptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If historical data is used for pricing insurance products, then pricing can be performed before products are sold, but the predictions fail to account for region-dependent and subgroup-specific risk factors, leading to inaccurate loss predictions
Solution Approach 1:
The patent segments the insured population into distinct subgroups based on risk characteristics, geographic regions, and other relevant factors. By dividing the homogeneous historical data into heterogeneous segments, the system captures region-dependent and subgroup-specific risk factors that were previously obscured, thereby improving prediction accuracy while maintaining the ability to price before products are sold.
Solution Approach 2:
The patent applies local quality by tailoring risk assessment parameters and pricing models to specific geographic regions and subgroups. Instead of using uniform historical averages, the system incorporates location-specific risk factors and subgroup characteristics into the pricing calculations, enabling accurate predictions that reflect local conditions.
2Ease of operation
If uniform pricing assumptions are used for all insured objects, then pricing process is simplified, but region-dependent and subgroup-specific risk factors are not captured, leading to suboptimal risk management
Solution Approach 1:
The patent implements dynamic pricing assumptions that automatically adjust based on the insured object's specific characteristics, geographic location, and risk profile. The system transitions from static uniform assumptions to dynamic, adaptive assumptions that are tailored to each insured object, improving risk management effectiveness while maintaining operational efficiency through automated classification and parameter assignment.
Data Source
AI summary
Forecasting frequencies associated with future loss and loss distributions for individual risks associated with operating units, each which have a measurable liability exposure, is accomplished via independently operated liability risk drivers. The frequencies associated with future loss and loss distributions are also forecasted for an automated operation of a loss resolving unit via a control unit controller. When a loss occurs at a loss unit, measure parameters are measured and transmitted to the control unit controller. The control unit controller dynamically assigns the measure parameters to the liability risk drivers and tunes the operation of the loss resolving unit by resolving the loss via the loss resolving unit.