Automated risk transfer system

• US 20130132163A1
• Filed: 01/13/2013
• Published: 05/23/2013
• Est. Priority Date: 10/17/2000
• Status: Abandoned Application

• Current Assignee: Jeffrey Eder
• Original Assignee: Jeffrey Eder
• Inventors: Eder, Jeff Scott
• Application Number:
  US13/740,223
• Publication Number:
  US 20130132163A1
• Time in Patent Office: Days
• US Class Current: 705/7.37
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

In computer-implemented methods, systems and program products for estimating financial modeling outcomes, financial data are segmented into a number of categories and scenario data for a set of model scenarios are processed to obtain an estimated model outcome distribution. The categories are mutually exclusive and collectively exhaustive of the financial data while the model may be developed by learning from the 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.

Claims

1. A computer-implemented method for performing an 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 of categories, wherein the categories 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;

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; and

outputting the estimated model outcome distribution.

2. The computer-implemented method of claim 1, wherein the processing further comprises:

selecting a first subset of the financial data as a first sample, the first subset drawn without replacement from each of the categories;

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;

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 categories;

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.

3. The computer-implemented method of claim 1, wherein the system comprises an organization that physically exists.

4. The computer-implemented method of claim 1, wherein the cumulative model outcome distribution is identified as the estimated model outcome distribution.

5. The computer-implemented method of claim 1, further comprising segmenting the financial data into the selected number categories before storing the financial data in the computer readable memory where the categories comprise current operation revenue, current operation expense, current operation capital change, real options and market sentiment.

6. The computer-implemented method of claim 1, wherein the financial data comprises insurance policyholder data;

wherein the model comprises a model of one or more insurance liabilities; and

wherein the model outcomes comprise measures of a risk comprising a likelihood of an occurrence of the insurance liability for a scenario in the set of scenarios.

7. The computer implemented method of claim 1, wherein the model of the system is developed by learning from the financial data where learning from the financial data comprises:

using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the financial data to use as an input when modeling an impact of each of one or more elements of value;

using the plurality of predictive models and the plurality of causal models to analyze and select a portion of the financial data to use as an input when modeling an impact each of one or more external factors;

learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model in order to model a net contribution or impact of each of the one or more elements of value and each of the one or more external factors to a model output value;

learning which model from a plurality of causal models comprises a best fit for modeling the contribution of the elements of value and the external factors to the model output value when using the selected data;

learning if a clustering of the input data improves an accuracy of the component of value models;

learning a relative contribution of each of the elements of value to the model output value, and

learning a relative contribution of each of the external factors to the model output value,

where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis, where the elements of value physically exist and are selected from the group consisting of customers, equipment, partners and suppliers and where the plurality of predictive models are selected from the group consisting of classification and regression tree;

projection pursuit regression;

generalized additive model (GAM), redundant regression network;

neural network, multivariate adaptive regression splines;

linear regression; and

stepwise regression.

8. A non-transitory computer program product tangibly embodied on a computer readable medium and comprising a program code for directing at least one computer to perform an automated risk management method, comprising:

store, in a computer readable memory, financial data related to a population of financial data records and segmented into a number of categories, wherein the categories 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;

provide 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;

process 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 where the model is developed by learning from the financial data; and

output the estimated model outcome distribution.

9. The non-transitory computer program product of claim 8, wherein the processing further comprises:

selecting a first subset of the financial data as a first sample, the first subset drawn without replacement from each of the categories;

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;

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 categories;

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.

10. The non-transitory computer program product of claim 8, wherein the system comprises an organization that physically exists.

11. The non-transitory computer program product of claim 8, wherein the cumulative model outcome distribution is identified as the estimated model outcome distribution.

12. The non-transitory computer program product of claim 8, further comprising segmenting the financial data into the selected number categories before storing the financial data in the computer readable memory where the categories comprise current operation revenue, current operation expense, current operation capital change, real options and market sentiment.

13. The non-transitory computer program product of claim 8, wherein the financial data comprises insurance policyholder data;

wherein the model comprises a model of one or more insurance liabilities; and

wherein the model outcomes comprise measures of a risk comprising a likelihood of an occurrence of the insurance liability for a scenario in the set of scenarios.

14. The non-transitory computer program product of claim 8, wherein developing the model of the system by learning from the financial data comprises:

using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the financial data to use as an input when modeling an impact of each of one or more elements of value;

using the plurality of predictive models and the plurality of causal models to analyze and select a portion of the financial data to use as an input when modeling an impact each of one or more external factors;

learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model in order to model a net contribution or impact of each of the one or more elements of value and each of the one or more external factors to a model output value;

learning which model from a plurality of causal models comprises a best fit for modeling the contribution of the elements of value and the external factors to the model output value when using the selected data;

learning if a clustering of the input data improves an accuracy of the component of value models;

learning a relative contribution of each of the elements of value to the model output value, and

learning a relative contribution of each of the external factors to the model output value,

where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree;

projection pursuit regression;

generalized additive model (GAM), redundant regression network;

neural network, multivariate adaptive regression splines;

linear regression; and

stepwise regression.

15. An automated risk management system, comprising:

a computer with at least one processor having circuitry to execute instructions;

a storage device available to each of said processors with sequences of instructions stored therein, which when executed cause at least one of the processors to;

store, in a computer readable memory, financial data related to a population of financial data records and segmented into a number of categories, wherein the categories 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;

provide a computer processor associated with the computer readable memory with a model of a tangible system defined at least in part by the set of variables;

process 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 tangible system and based on the set of scenarios where the model is developed by learning from the financial data; and

output the estimated model outcome distribution.

16. The system of claim 15, wherein the processing further comprises:

selecting a first subset of the financial data as a first sample, the first subset drawn without replacement from each of the categories;

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;

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 categories;

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.

17. The system of claim 15, wherein the tangible system comprises an organization that physically exists.

18. The system of claim 15, wherein the cumulative model outcome distribution is identified as the estimated model outcome distribution.

19. The system of claim 15, further comprising segmenting the financial data into the selected number categories before storing the financial data in the computer readable memory where the categories comprise current operation revenue, current operation expense, current operation capital change, real options and market sentiment.

20. The system of claim 15, wherein developing the model of the system by learning from the financial data comprises:

using a plurality of predictive models and a plurality of causal models to analyze and select a portion of the financial data to use as an input when modeling an impact of each of one or more elements of value;

using the plurality of predictive models and the plurality of causal models to analyze and select a portion of the financial data to use as an input when modeling an impact each of one or more external factors;

learning which algorithm from a plurality of linear and nonlinear predictive model algorithms to include in the model in order to model a net contribution or impact of each of the one or more elements of value and each of the one or more external factors to a model output value;

learning which model from a plurality of causal models comprises a best fit for modeling the contribution of the elements of value and the external factors to the model output value when using the selected data;

learning if a clustering of the input data improves an accuracy of the component of value models;

learning a relative contribution of each of the elements of value to the model output value, and

learning a relative contribution of each of the external factors to the model output value,

where the plurality of causal models are selected from the group consisting of Tetrad, LaGrange, Bayesian and path analysis and where the plurality of predictive models are selected from the group consisting of classification and regression tree;

projection pursuit regression;

generalized additive model (GAM), redundant regression network;

neural network, multivariate adaptive regression splines;

linear regression; and

stepwise regression.