Insurance Liability Stochastic Modeling via Stratified Sampling
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Solution Overview
Problem
Current actuarial models for insurance liabilities are computationally complex and time-consuming, especially when analyzing entire populations, which limits the number of risk scenarios that can be tested and makes real-time risk assessment challenging, while analyzing subsets may not provide results accurate enough to acceptable tolerance levels.
Innovation Solution
A computer-implemented method and system that segments financial data into mutually exclusive and exhaustive classes, processes these data using a model defined by variables, and performs multiple tests with subsets to achieve a cumulative model outcome distribution within a predetermined tolerance limit, allowing for efficient estimation of insurance liability risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire population of insurance policyholders is analyzed using stochastic modeling, then the accuracy of risk assessment is improved, but the computational time and complexity increase significantly
Solution Approach 1:
The patent segments the entire population of insurance policyholders into multiple strata or groups based on common characteristics (e.g., demographic factors, policy types, risk profiles). By analyzing each stratum separately and combining results, the method achieves population-level accuracy while reducing the computational burden of analyzing every individual policyholder in a single monolithic model.
Solution Approach 2:
The patent creates synthetic copies or representative samples from each stratum that preserve the statistical properties of the original population segments. These copied representations are then used in stochastic modeling to estimate risk metrics, allowing the model to generalize findings from sampled data to the entire population without processing every individual record.
2Productivity
If a subset of the population is analyzed to reduce computational time, then the processing speed is improved, but the accuracy of results deteriorates
Solution Approach 1:
The population is divided into homogeneous strata, and samples are drawn from each stratum proportionally or optimally. This segmented sampling approach ensures that the subset analyzed represents the diversity and distribution of the entire population, maintaining accuracy while enabling faster processing compared to analyzing the full population.
Solution Approach 2:
The patent adjusts sampling parameters such as sample size per stratum, sampling probability, and confidence level thresholds to optimize the balance between processing speed and result accuracy. By dynamically changing these parameters based on stratum characteristics and risk importance, the method achieves acceptable accuracy with reduced computational effort.
Data Source
AI summary
In computer-implemented methods and systems for estimating financial modeling outcomes, financial data segmented into a number (x) of classes and scenario data for a set of model scenarios are processed to obtain an estimated model outcome distribution. The class segments are mutually exclusive and collectively exhaustive of the financial data. Multiple model tests are performed with samples of the financial data until a cumulative model outcome distribution is within a pre-determined acceptable tolerance limit from a distribution of fully assessed model outcomes obtainable by performing a single test of the scenarios using all of the financial data. The number (x) of classes, the sample size (z), and a number (y) of times that the tests are performed ensure that the cumulative model outcome distribution is within the pre-determined acceptable tolerance limit from the distribution of fully assessed model outcomes.