Transforming data for rendering an insurability decision

• US 8,775,218 B2
• Filed: 10/17/2011
• Issued: 07/08/2014
• Est. Priority Date: 05/18/2011
• Status: Active Grant

• Current Assignee: RGA Reinsurance Co. (New Zealand)
• Original Assignee: RGA Reinsurance Co. (New Zealand)
• Inventors: Burgoon, J. David Jr., Snell, David L., Wehrman, Susan L.
• Primary Examiner(s): Shaikh, Mohammad Z
• Application Number:
  US13/274,869
• Publication Number:
  US 20120296676A1
• Time in Patent Office: 995 Days
• Field of Search: 705/4, 705 35- 45
• US Class Current: 705/4
• CPC Class Codes: F04C 2270/041 Controlled or regulated
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 70/00 ICT specially adapted for the handling or processing of medical references

Abstract

Transformation of disparate data for use in rendering a decision involving a potentially insurable risk. An Extract, Transform, Load (ETL) process extracts the data and converts it from a plurality of formats into a standard format for processing. A heuristic engine inferentially processes the converted data to identify information relevant to the decision to be rendered. A consolidation and presentation engine generates presentable knowledge from the relevant information and then presents the knowledge to a decision-making entity for rendering the decision. And an optimization feedback process monitors one or more actions on the presented knowledge by the decision-making entity and adjusts one or more of the ETL process, the heuristic engine, and the consolidation and presentation engine as a function of the monitored actions.

Claims

1. A method of structuring and transforming disparate data for use in rendering a decision involving a potentially insurable applicant, said method comprising:

retrieving, by a computer, data from a first database, said retrieved data relating to the applicant; and

executing, by the computer, computer-executable instructions for;

transforming the retrieved data into domain-specific information, said domain-specific information relating to insurability of the applicant;

storing the transformed domain-specific information in a second database;

defining one or more relevancy factors as a function of the decision to be rendered involving the applicant;

assigning at least one of the relevancy factors to at least a portion of the transformed domain-specific information stored in the second database;

processing the transformed domain-specific information as a function of the assigned relevancy factors;

providing an output comprising the assigned relevancy factors to an underwriter for use in rendering the decision involving the applicant, wherein the processing, by the computer, comprises at least one of;

ant colony optimization, a heuristic algorithm, network theory, predictive modeling, deterministic chaos, behavioral economics, fractal geometry, and cellular automata;

monitoring one or more actions on the provided output by the underwriter in rendering the decision;

generating feedback as a function of the monitored actions;

adjusting, by the computer, at least one of said transforming, defining, assigning, and processing the transformed domain-specific information subsequent to providing the feedback to the underwriter; and

presenting an updated output to the underwriter based on the feedback.

2. The method of claim 1, wherein transforming the retrieved data into domain-specific information comprises executing a domain-specific Extract, Transform, Load (ETL) process to extract the retrieved data and convert the extracted data into one or more standard formats.

3. The method of claim 1, wherein the data stored in the first database comprises one or more of the following types of data:

applicant-provided data, electronic medical records data, electronic health records data, continuity of care records data, prescription data, other medical sources data, financial sources data, motor vehicle records data, and other non-medical sources data.

4. The method of claim 1, wherein assigning the at least one of the relevancy factors comprises executing a heuristic engine on the information stored in the second database to infer risk assessment relationships among the information.

5. The method of claim 1, wherein the retrieved data comprises one or more of the following types of complex data:

social network data and datamart data.

6. The method of claim 5, further comprising executing a data mining process on the complex data to identify covariance relationships among the data.

7. The method of claim 6, wherein the data mining process comprises predictive modeling.

8. The method of claim 1, wherein one or more computer-readable media have computer-executable instructions stored thereon for performing the method of claim 1.

9. A system of structuring and transforming disparate data for use in rendering a decision involving a potentially insurable applicant, said system comprising:

a first database storing data relating to the applicant; and

a computer configured to execute computer-executable instructions for;

transforming data retrieved from the first database into domain-specific information, said domain-specific information relating to insurability of the applicant;

storing the transformed domain-specific information in a second database;

defining one or more relevancy factors as a function of the decision to be rendered involving the applicant;

assigning at least one of the relevancy factors to at least a portion of the transformed domain-specific information stored in the second database;

processing the transformed domain-specific information as a function of the assigned relevancy factors;

providing an output comprising the assigned relevancy factors to an underwriter for use in rendering the decision involving the applicant, wherein the processing comprises at least one of;

ant colony optimization, a heuristic algorithm, network theory, predictive modeling, deterministic chaos, behavioral economics, fractal geometry, and cellular automata;

monitoring one or more actions on the provided output by the underwriter in rendering the decision;

generating feedback as a function of the monitored actions;

adjusting, by the computer, at least one of said transforming, defining, assigning, and processing the transformed domain-specific information subsequent to providing the feedback to the underwriter; and

presenting an updated output to the underwriter based on the feedback.

10. The system of claim 9, wherein transforming the retrieved data into domain-specific information comprises executing a domain-specific Extract, Transform, Load (ETL) process to extract the retrieved data and convert the extracted data into one or more standard formats.

11. The system of claim 9, wherein the data stored in the first database comprises one or more of the following types of data:

applicant-provided data, electronic medical records data, electronic health records data, continuity of care records data, prescription data, other medical sources data, financial sources data, motor vehicle records data, and other non-medical sources data.

12. The system of claim 9, wherein assigning the at least one of the relevancy factors comprises executing a heuristic engine on the information stored in the second database to infer risk assessment relationships among the information.

13. The system of claim 9, wherein the data retrieved from the first database comprises one or more of the following types of complex data:

social network data and datamart data.

14. The system of claim 13, wherein the computer is further configured to execute computer-executable instructions for executing a data mining process on the complex data to identify covariance relationships among the data.

15. The system of claim 14, wherein the data mining process comprises predictive modeling.

16. A non-transitory computer-readable medium storing computer-executable instructions, which instructions when executed by a computer cause the computer to structure and transform disparate data for use in rendering a decision involving a potentially insurable applicant, said computer-executable instructions comprising:

transforming data relating to the applicant into domain-specific information, said domain-specific information relating to insurability of the applicant;

defining one or more relevancy factors as a function of the decision to be rendered involving the applicant;

assigning at least one of the relevancy factors to at least a portion of the transformed domain-specific information;

processing the transformed domain-specific information as a function of the assigned relevancy factors;

providing an output comprising the assigned relevancy factors to an underwriter for use in rendering the decision involving the applicant, wherein the processing comprises at least one of;

ant colony optimization, a heuristic algorithm, network theory, predictive modeling, deterministic chaos, behavioral economics, fractal geometry, and cellular automata;

monitoring one or more actions on the provided output by the underwriter in rendering the decision;

generating feedback as a function of the monitored actions;

adjusting at least one of said transforming, defining, assigning, and processing the transformed domain-specific information subsequent to providing the feedback to the underwriter; and

presenting an updated output to the underwriter based on the feedback.

17. The computer-readable medium of claim 16, wherein transforming the retrieved data into domain-specific information comprises executing a domain-specific Extract, Transform, Load (ETL) process to extract the data and convert the extracted data into one or more standard formats.

18. The computer-readable medium of claim 16, wherein the data comprises one or more of the following types of data:

applicant-provided data, electronic medical records data, electronic health records data, continuity of care records data, prescription data, other medical sources data, financial sources data, motor vehicle records data, and other non-medical sources data.

19. The computer-readable medium of claim 16, wherein assigning the at least one of the relevancy factors comprises executing a heuristic engine on the transformed domain-specific information to infer risk assessment relationships among the information.

20. The computer-readable medium of claim 16, wherein the data comprises one or more of the following types of complex data:

social network data and datamart data.

21. The computer-readable medium of claim 20, wherein the computer-executable instructions further comprise executing a data mining process on the complex data to identify covariance relationships among the data.

22. The computer-readable medium of claim 21, wherein the data mining process comprises predictive modeling.