Risk modeling system
First Claim
1. A modeling system that operates on an initial data collection which includes risk factors and outcomes, comprising:
- data storage for a plurality of risk factors and outcomes that are associated with the risk factors;
a library of algorithms that operate to test variable interactions between the risk factors and results to confirm statistical validity of the associations;
optimization logic that forms and tunes ensembles by receiving groups of risk factors, selecting predetermined design patterns for calculations at respective ensemble parts according to a set of predefined rules, and relating the respective parts of the ensemble to establish required data flow between the respective components;
the optimization logic operating to form a plurality of such ensembles on an iterative basis, test the ensembles for fitness, and select the best ensemble for use as a production model; and
means for interacting with the production risk model to perform business operations using the production risk model as a predictive tool.
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Abstract
A system including a general-purpose decision support and decision making predictive analytics engine that is able to find patterns in many types of digitally represented data. Given data that represents a random collection of points, the system finds these internal patterns employing an inductive principle called structural risk minimization that separates the points with the maximum margin. Internal patterns in the initial data are inductively determined by employing structural risk minimization to separate the points with a maximum margin. A model based on the internal patterns in the data is then generated, and the model is used with new data to generate predictions by evaluating the new data for similarities to the model. The model is implemented to facilitate decision making processes. Special features are provided to validate incoming data, preprocess the data, and monitor the data to improve the integrity of modeling results. Results are delivered to users by a reporting capability that facilitates the decision making processes that are inherent to a business enterprise.
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Citations
49 Claims
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1. A modeling system that operates on an initial data collection which includes risk factors and outcomes, comprising:
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data storage for a plurality of risk factors and outcomes that are associated with the risk factors;
a library of algorithms that operate to test variable interactions between the risk factors and results to confirm statistical validity of the associations;
optimization logic that forms and tunes ensembles by receiving groups of risk factors, selecting predetermined design patterns for calculations at respective ensemble parts according to a set of predefined rules, and relating the respective parts of the ensemble to establish required data flow between the respective components;
the optimization logic operating to form a plurality of such ensembles on an iterative basis, test the ensembles for fitness, and select the best ensemble for use as a production model; and
means for interacting with the production risk model to perform business operations using the production risk model as a predictive tool. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A method of modeling operates on an initial data collection which includes risk factors and outcomes, comprising:
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storing data for a plurality of risk factors and outcomes that are associated with the risk factors;
accessing a library of algorithms that operate to test associations between the risk factors and results to confirm statistical validity of the associations;
creating an ensemble for optimization by receiving groups of risk factors, selecting predetermined design patterns for calculations at respective ensemble parts according to a set of predefined rules, and relating the respective parts of the ensemble to establish required data flow between the respective components;
tuning the ensemble by iteration to form a plurality of new ensembles, testing the ensembles for fitness, and selecting the best ensemble for use in a risk model. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39)
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40. A method of collectively evaluating multiple risk factors for insurance underwriting, comprising:
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receiving a plurality of risk factors and outcomes associated with the risk factors;
selecting at least one algorithm from a library of algorithms, each algorithm operable to test associations between the risk factors and associated results to confirm statistical validity of the association and identify the risk factors with the most predictive information;
selecting a subset of risk factors having the greatest predictive information as at least on ensemble, selecting predetermined design patterns for calculations at respective ensemble parts according to a set of predefined rules, and relating the respective parts of the ensemble to establish required data flow between the respective components;
tuning the ensemble by iteration to form a plurality of new ensembles, the iteration including;
creating a candidate model based on a set of model parameters;
evaluating the candidate model at least once with respect to model fitness;
in response to the evaluation, adjusting the model parameters;
repeating the creation of the candidate model until an optimal model is found; and
testing the new ensembles for fitness, and selecting the most fit ensemble for use as a risk model for insurance underwriting. - View Dependent Claims (41, 42, 43, 44, 45, 46, 47, 48, 49)
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Specification