METHOD AND SYSTEM FOR EVALUATING INSURANCE LIABILITIES USING STOCHASTIC MODELING AND SAMPLING TECHNIQUES
First Claim
1. A computer-implemented method for performing analysis of financial data, comprising the steps of:
- storing, in a computer readable memory,financial data related to a population of financial data records and segmented into a number (x) of classes, wherein the class segments are mutually exclusive and collectively exhaustive of the financial data, andscenario data for a set of scenarios, wherein each of the scenarios is defined at least in part by a set of variables, and wherein the scenario data for each of the scenarios comprise at least some parameter values for the set of variables;
providing a computer processor associated with the computer readable memory with a model of a system defined at least in part by the set of variables; and
processing, with the computer processor, the financial data and the scenario data using the model to obtain an estimated model outcome distribution comprising a distribution of estimated model outcomes relating to the system and based on the set of scenarios, the processing further comprising;
selecting a first subset of the financial data as a first sample, the first subset drawn without replacement from each of the class segments of the population and having a sample size (z);
performing, with the model and the first subset, a first test of the set of scenarios to obtain a first set of sample outcomes for each of the scenarios; and
repeating the selecting and performing steps using additional subsets of the financial data to perform additional tests of the set of scenarios and to obtain additional sets of sample outcomes for each of the scenarios;
wherein the additional subsets are drawn without replacement from each of the class segments;
wherein the first sample outcomes and the additional sample outcomes are combined to create a cumulative estimated model outcome distribution;
wherein the selecting and performing steps are repeated until the cumulative model outcome distribution is within a pre-determined acceptable tolerance limit from a distribution of fully assessed model outcomes for the set of scenarios obtainable by performing, with the model, a single test of the set of scenarios using all of the data; and
wherein the cumulative model outcome distribution is identified as the estimated model outcome distribution;
wherein the number (x) of classes, the sample size (z), and a number (y) of tests comprising a count of the number of times that the performing step is conducted ensure that the cumulative model outcome distribution is within the pre-determined acceptable tolerance limit from the distribution of fully assessed model outcomes.
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Abstract
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.
38 Citations
20 Claims
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1. A computer-implemented method for performing analysis of financial data, comprising the steps of:
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storing, in a computer readable memory, financial data related to a population of financial data records and segmented into a number (x) of classes, wherein the class segments are mutually exclusive and collectively exhaustive of the financial data, and scenario data for a set of scenarios, wherein each of the scenarios is defined at least in part by a set of variables, and wherein the scenario data for each of the scenarios comprise at least some parameter values for the set of variables; providing a computer processor associated with the computer readable memory with a model of a system defined at least in part by the set of variables; and processing, with the computer processor, the financial data and the scenario data using the model to obtain an estimated model outcome distribution comprising a distribution of estimated model outcomes relating to the system and based on the set of scenarios, the processing further comprising; selecting a first subset of the financial data as a first sample, the first subset drawn without replacement from each of the class segments of the population and having a sample size (z); performing, with the model and the first subset, a first test of the set of scenarios to obtain a first set of sample outcomes for each of the scenarios; and repeating the selecting and performing steps using additional subsets of the financial data to perform additional tests of the set of scenarios and to obtain additional sets of sample outcomes for each of the scenarios; wherein the additional subsets are drawn without replacement from each of the class segments; wherein the first sample outcomes and the additional sample outcomes are combined to create a cumulative estimated model outcome distribution; wherein the selecting and performing steps are repeated until the cumulative model outcome distribution is within a pre-determined acceptable tolerance limit from a distribution of fully assessed model outcomes for the set of scenarios obtainable by performing, with the model, a single test of the set of scenarios using all of the data; and wherein the cumulative model outcome distribution is identified as the estimated model outcome distribution; wherein the number (x) of classes, the sample size (z), and a number (y) of tests comprising a count of the number of times that the performing step is conducted ensure that the cumulative model outcome distribution is within the pre-determined acceptable tolerance limit from the distribution of fully assessed model outcomes. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification