Risk modeling system

• US 20070016542A1
• Filed: 07/01/2006
• Published: 01/18/2007
• Est. Priority Date: 07/01/2005
• Status: Abandoned Application

• Current Assignee: Valen Technologies, Inc. (Insurity Holdings, Inc.)
• Original Assignee: Valen Technologies, Inc. (Insurity Holdings, Inc.)
• Inventors: Rosauer, Matt, Vlasimsky, Richard
• Application Number:
  US11/479,803
• Publication Number:
  US 20070016542A1
• Time in Patent Office: Days
• US Class Current: 706/21
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• 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 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 40/08 Insurance

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.

Claims

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.

2. The system of claim 1, further comprising means for deploying the risk model on a production basis to underwrite insurance policies.

3. The system of claim 1, wherein the predefined rules implement a pattern of temporal boost by competitively evaluating long term, medium term, and short term sample sets.

4. The system of claim 1, wherein the predefined rules implement a sequencer that provides a temporal abstract to shift a variable over time.

5. The system of claim 1, wherein the predefined rules implement an automated data enrichment preprocessor that performs external data including at least one data type selected external data at least selected from the group consisting of firmagraphic, demographic, demographic, econometric, geographic, weather, legal, vehicle, industry, driver, property, and geo-location data

6. The system of claim 1, further comprising means for blind validation to confirm the risk model that is produce by the optimization logic.

7. The system of claim 1 implemented on a grid architecture that permits at least one master to assign discrete tasks to a plurality of workers.

8. The system of claim 7, further comprising a web-services interface to end users of the risk model.

9. The system of claim 1, wherein the risk model operates to provide risk scoring for insurance underwriting purposes.

10. The system of claim 1, wherein the risk model operates to provide rating for insurance underwriting and actuarial purposes.

11. The system of claim 1, wherein the risk model operates to provide tier placement for insurance underwriting and actuarial purposes.

12. The system of claim 1, wherein the risk model operates to provide risk segmentation for insurance underwriting and actuarial purposes.

13. The system of claim 1, wherein the risk model operates to provide risk selection for insurance underwriting and actuarial purposes.

14. The system of claim 1, wherein the optimization logic iterates in stages that include:

(a) creating a candidate model;

(b) evaluating the model with respect to model fitness;

(c) re-evaluating the model with respect to model fitness; and

(d) repeating steps (a) through (c) using a new set of model parameter permutations until an optimal model is found.

15. The system of claim 1, further comprising means for monitoring new data that is used for predictive purposes by comparison to statistical parameters of data upon which the predictive model is based.

16. The system of claim 1, further comprising means for enriching data that is used in the risk model by reporting from a plurality of data sources and preprocessing the data to provide values that are useful to the model.

17. The system of claim 1, further comprising screening filter logic for reporting from the data storage on the basis of one or more selection parameters to identify a subset of risk factors and outcomes for submission to the library of algorithms and the optimization logic.

18. The system of claim 17, wherein the screening filter logic is automated to provide screening by the use of a rule.

19. The system of claim 17, wherein the screening filter logic operates on a plurality of selection parameters.

20. A method of modeling operates on an initial data collection which includes risk factors and outcomes, comprising:

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.

21. The method of claim 20, further comprising a step of deploying the risk model on a production basis to underwrite insurance policies.

22. The method of claim 20, wherein the predefined rules implement a pattern of temporal boost by competitively evaluating long term, medium term, and short term business goals.

23. The method of claim 20, wherein the predefined rules used in the step of creating an ensemble implement a sequencer pattern by comparing the predictive accuracy of risk factor data that is accumulated for analysis over a period of time.

24. The method of claim 20, wherein the predefined rules implement an automated data enrichment preprocessor that performs deterministic and probabilistic matches to external data including at least one data type selected from the group consisting of firmagraphic, demographic, demographic, econometric, geographic, weather, legal, vehicle, industry, driver, property, and geo-location data

25. The method of claim 20, wherein the predefined rules used in the step of creating an ensemble implement a functional boost pattern that uses a segmented model to develop a plurality of functional expert models, that are later recombined as a committee of experts.

26. The method of claim 20, further comprising a step of validating, by the use of a separate dataset other than a dataset used to create the ensemble, to confirm the risk model that is produced by the optimization logic.

27. The method of claim 20, further comprising implementing the risk model on a grid architecture that permits at least one master to assign discrete tasks to a plurality of workers.

28. The method of claim 20, further comprising a step of providing access to end users of the risk model by use of a web-based architecture.

29. The method of claim 20, wherein the risk model operates to provide risk scoring for insurance underwriting purposes.

30. The method of claim 20, wherein the risk model operates to provide rating for insurance underwriting and actuarial purposes.

31. The method of claim 20, wherein the risk model operates to provide tier placement for insurance underwriting and actuarial purposes.

32. The method of claim 20, wherein the risk model operates to provide risk segmentation for insurance underwriting and actuarial purposes.

33. The method of claim 20, wherein the risk model operates to provide risk selection for insurance underwriting and actuarial purposes.

34. The method of claim 20, wherein the step of iterating entails:

(a) creating a candidate model;

(b) evaluating the model with respect to model fitness;

(c) re-evaluating the model with respect to model fitness; and

(d) repeating steps (a) through (c) using a new set of model parameter permutations until an optimal model is found.

35. The method of claim 20, further comprising monitoring new data that is used for predictive purposes by comparison to statistical parameters of data upon which the predictive model is based.

36. The method of claim 20, further comprising enriching data that is used in the risk model by reporting from a plurality of data sources and preprocessing the data to provide values that are useful to the model.

37. The method of claim 20, further comprising screening the data storage on the basis of one or more selection parameters to identify a subset of risk factors and outcomes for use in the accessing, creating and tuning steps.

38. The method of claim 37, wherein the screening filter logic is automated to provide screening by the use of a rule.

39. The method of claim 37, wherein the screening filter logic operates on a plurality of selection parameters.

40. A method of collectively evaluating multiple risk factors for insurance underwriting, comprising:

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.

41. The method of claim 40, wherein at least one subset of risk factors is a target model for use by an optimization engine to initial model parameters.

42. The method of claim 40, wherein the predefined rules used in the step of creating an ensemble implement a sequencer pattern by comparing the predictive accuracy of risk factor data that is accumulated for analysis over a period of time.

43. The method of claim 40, wherein the predefined rules implement a an automated data enrichment preprocessor that performs deterministic and probabilistic matches to external data including at least one data type selected from the group consisting of firmagraphic, demographic, demographic, econometric, geographic, weather, legal, vehicle, industry, driver, property, geo-location data, and combinations thereof.

44. The method of claim 40, wherein the predefined rules used in the step of creating an ensemble implement a functional boost pattern of temporal boost by competitively evaluating long term, medium term, and short term sample sets.

45. The method of claim 40, further comprising a step of validating, by the use of a separate dataset other than a dataset used to create the ensemble, to confirm the risk model that is produced by the optimization logic.

46. The method of claim 40, further comprising implementing the risk model on a grid architecture that permits at least one master to assign discrete tasks to a plurality of workers.

47. The method of claim 40, wherein the step of selecting a subset of risk factors includes screening the risk factors and outcomes on the basis of one or more selection parameters to identify a subset of risk factors and outcomes for use in the accessing, creating and tuning steps.

48. The method of claim 47, wherein the screening filter logic is automated to provide screening by the use of a rule.

49. The method of claim 47, wherein the screening filter logic operates on a plurality of selection parameters.