Dynamic Risk Evaluation Using Weighted Factors
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Solution Overview
Problem
Traditional methods for determining mortality risk in life insurance policies face challenges such as limited data, inherent biases, static assessments, uncertainty, and reliance on outdated technology, leading to inaccurate risk assessments and lack of automation in premium adjustments.
Innovation Solution
A decision intelligence (DI)-based computerized framework that utilizes advanced data analytics, machine learning, and predictive modeling to assess mortality risk dynamically, allowing for automated premium adjustments based on individual changes in health and lifestyle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional methods using limited data sources and statistical tables are used, then the risk assessment process is simplified, but the accuracy of mortality risk assessment deteriorates and individual variations are not considered
Solution Approach 1:
The risk assessment process is segmented into multiple independent modules: data collection module (gathering data from diverse sources), data processing module (cleaning and transforming data), risk calculation module (computing risk metrics), and visualization module (presenting results). This segmentation allows complex individual variations to be handled through modular processing while maintaining overall system manageability.
Solution Approach 2:
The system transitions from traditional two-dimensional statistical tables (age, gender) to a multi-dimensional assessment framework incorporating numerous variables including lifestyle factors, health metrics, genetic information, and environmental data. This dimensional expansion enables comprehensive capture of individual variations while the automated processing keeps the complexity manageable.
2Ease of manufacture
If traditional actuarial tables are used, then the underwriting process is straightforward, but inherent biases against certain demographic groups occur leading to discrimination in insurance pricing
Solution Approach 1:
The system applies local quality by customizing risk assessments for each individual based on their specific characteristics and circumstances. Instead of applying uniform actuarial tables to all individuals, the system adjusts parameters and weightings based on actual data from each person's lifestyle, health status, and environmental factors, thereby eliminating discriminatory practices while maintaining fair pricing.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor and adjust risk assessments based on actual outcomes and changing conditions. This feedback loop allows the system to learn from real-world data and refine its algorithms, ensuring that pricing decisions are based on actual risk patterns rather than biased statistical generalizations, thereby preventing discrimination.
3Device complexity
If static assessments are made at the time of policy issuance, then the underwriting process is simple, but the assessments do not adapt to changes in the policyholder's health or lifestyle over time
Solution Approach 1:
The system implements dynamics by transitioning from static underwriting to dynamic continuous assessment. The system automatically updates risk profiles based on real-time data from wearables, health records, and lifestyle trackers. This dynamic approach allows the insurance product to adapt continuously to changing health and lifestyle conditions, providing both simplicity through automation and high adaptability through continuous monitoring.
Solution Approach 2:
The system maintains continuity of useful action through ongoing data collection and periodic re-assessment of policyholders. Rather than a one-time assessment, the system continuously monitors health metrics, lifestyle factors, and risk indicators, ensuring that the risk profile remains current. This continuous action provides both operational simplicity through automation and adaptability through uninterrupted updates.
4Productivity
If automated premium adjustment is implemented, then responsiveness to risk changes is improved, but the system complexity increases requiring advanced data analytics and machine learning
Solution Approach 1:
The system implements self-service by enabling automated premium adjustments based on objective risk data without requiring manual underwriter intervention. The machine learning models automatically process new data, update risk profiles, and calculate premium changes, allowing the system to serve itself. This automation provides rapid responsiveness to risk changes while the transparent, data-driven approach manages complexity through standardized algorithms rather than human judgment.
Data Source
AI summary
The present disclosure relates to servers, systems, and methods for dynamic risk evaluation using weighted factors. It introduces a decision intelligence-based framework for assessing risk. Traditional methods face challenges like limited data, biases, and static assessments, where the system described herein leverages advanced data analytics, machine learning, and/or predictive modeling to enhance accuracy and personalization. In some embodiments, the system is configured to enable users to enter parameters, targeting criteria, and/or factors for tracking. The system performs risk determination, outputs premiums, and automatically monitors changes in factors over time. In some embodiments, the system enables weightings for factors to be applied automatically, where in some embodiments, AI model are configured to generate the weightings based on predictions. Example factors include heart rate, BMI, and sleep time. In some embodiments, the system is configured to change insurance premiums without human intervention based on changes in life events and/or activity.