Personalized Actuarial Tables via Cognitive Test Analysis
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Solution Overview
Problem
Current insurance underwriting methods rely heavily on historical medical records, making it difficult for individuals with certain conditions to obtain insurance, as they are deemed higher risks based on static actuarial tables that do not account for future health prospects.
Innovation Solution
A tool that analyzes cognitive and physical test results to generate personalized actuarial tables by applying statistical shifts to existing mortality and disability tables, adjusting probabilities based on individual performance, thereby creating tailored insurance plans for candidates who may otherwise be uninsurable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static actuarial tables based on historical medical records are used, then insurance risk assessment is simplified and processing speed is improved, but individuals with certain conditions are unfairly deemed higher risks and lose access to insurance
Solution Approach 1:
The patent transforms static actuarial tables into dynamic, personalized risk assessments by introducing cognitive and physical test results that update the risk profile for each individual candidate. The system calculates personalized actuarial tables by applying statistical shifts to base tables based on test performance, making the risk assessment adaptable to each candidate's actual functional capacity rather than relying on fixed historical data
Solution Approach 2:
The patent changes the parameters used for risk assessment from historical medical conditions to current cognitive and physical test results. By measuring actual functional capacity through standardized tests and translating these into statistical shifts, the system modifies the risk parameters dynamically for each candidate, allowing those with good functional capacity to obtain insurance despite historical conditions
2Measurement precision
If traditional actuarial tables are used that account for historical conditions, then risk assessment accuracy for high-risk groups is improved, but accessibility to insurance for individuals with certain conditions deteriorates
Solution Approach 1:
The patent introduces feedback mechanisms where cognitive and physical test results directly influence the personalized actuarial table calculation. The test performance provides real-time feedback on actual functional capacity, which is then translated into statistical shifts that adjust the risk assessment. This feedback loop allows individuals to demonstrate their current capabilities and potentially overcome limitations indicated by historical medical records
Solution Approach 2:
The patent performs preliminary assessment through cognitive and physical tests before finalizing the insurance decision. By evaluating actual functional capacity in advance through standardized measurements, the system can accurately assess risk based on current capabilities rather than historical conditions, enabling more individuals to qualify for insurance while maintaining appropriate risk pricing
Data Source
AI summary
A tool for test-based updating of actuarial tables includes an analyzer, a statistical transformer and an actuarial analyzer. The analyzer analyzes the results of cognitive and physical tests performed on a candidate of a known age and generates cognitive and physical scores for the results. The statistical transformer utilizes the scores and the known age to generate a statistical shift and generates adjusting factors Fi for the actuarial tables from the statistical shift. The actuarial analyzer raises the mortality probabilities of the actuarial tables by a power of 1/Fi per table, thereby to produce personalized actuarial tables for the candidate.