Transforming data for rendering an insurability decision

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.

1 . A computerized method of transforming disparate data for use in rendering a decision involving a potentially insurable applicant, said method comprising:
 receiving data relating to the applicant from a plurality of sources, said data being stored in a memory in a plurality of formats;   accessing, by a computer, the received data stored in the memory; and   executing, by the computer, computer-executable instructions for:
 extracting the received data and converting the extracted data into one or more standard formats, said converted data relating to insurability of the applicant; 
 filtering the converted data by one or more relevancy factors assigned to the converted data, said relevancy factors being a function of a decision to be rendered by an underwriter regarding the insurability of the applicant; 
 generating, from the filtered data, presentable knowledge; 
 presenting the knowledge to the underwriter for rendering the decision; 
 monitoring one or more actions on the presented knowledge by the underwriter; and 
 adjusting, by the computer, one or more of said extracting, converting, filtering, and generating presentable knowledge as a function of the monitored actions. 
   
     
     
         2 . The method of  claim 1 , wherein extracting the received data and converting the extracted data into one or more standard formats comprises executing a domain-specific Extract, Transform, Load (ETL) process on the received data. 
     
     
         3 . The method of  claim 1 , wherein the received data comprises one or more of the following types of data: applicant-provided data, electronic medical 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 filtering the converted data by one or more relevancy factors comprises executing a heuristic engine for inferring risk assessment relationships among the converted data. 
     
     
         5 . The method of  claim 4 , further comprising storing the converted data in a staging area repository, and wherein the heuristic engine processes the data stored in the staging area repository. 
     
     
         6 . The method of  claim 1 , wherein the received data comprises one or more of the following types of complex data: social network data and datamart data. 
     
     
         7 . The method of  claim 6 , further comprising executing a data mining process on the complex data to identify covariance relationships among the data. 
     
     
         8 . The method of  claim 7 , wherein the data mining process comprises predictive modeling. 
     
     
         9 . The method of  claim 1 , wherein adjusting one or more of said extracting, converting, filtering, and generating presentable knowledge as a function of the monitored actions of the decision making entity as a component of the process comprises executing a metaheuristic optimization algorithm to assist in refinement of the presenting. 
     
     
         10 . The method of  claim 1 , wherein presenting the knowledge to the underwriter comprises executing a consolidation and presentation engine to present a summary of relevant information to the underwriter. 
     
     
         11 . 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 . 
     
     
         12 . The method of  claim 1 , wherein generating presentable knowledge further comprises executing at least one of: ant colony optimization, a heuristic algorithm, network theory, predictive modeling, deterministic chaos, behavioral economics, fractal geometry, and cellular automata. 
     
     
         13 . The method of  claim 1 , further comprising the mapping of data to a database, wherein the adjusting further comprises a feedback system based on the consumption or modification of the data that is further used to refine and adjust the mapping of data to the database. 
     
     
         14 . A non-transitory computer-readable medium storing computer-executable instructions that, when executed, transform disparate data for use in rendering a decision involving a potentially insurable applicant, said computer-readable medium comprising:
 data from a plurality of sources and in a plurality of formats;   an Extract, Transform, Load (ETL) process for extracting the data and converting the data from the plurality of formats into one or more standard formats;   a processing engine for inferentially processing the converted data to identify information relevant to the decision to be rendered, wherein the processing engine implements at least one of: ant colony optimization, a heuristic algorithm, network theory, predictive modeling, deterministic chaos, behavioral economics, fractal geometry, and cellular automata;   a consolidation and presentation engine for generating presentable knowledge comprising the identified relevant information and presenting the knowledge to an underwriter for use in rendering the decision;   an optimization feedback process for monitoring one or more actions on the presented knowledge by the underwriter and adjusting one or more of the ETL process, the processing engine, and the consolidation and presentation engine to present an optimized update of the presented knowledge to the underwriter as a function of the monitored actions.   
     
     
         15 . The computer-readable medium of  claim 14 , wherein the received data comprises one or more of the following types of data: applicant-provided data, financial sources data, electronic medical records data, electronic health records data, continuity of care records data, prescription data, other medical sources data, financial sources data, social network data, motor vehicle records data, other non-medical sources data, and datamart data. 
     
     
         16 . A system comprising:
 a memory storing disparate data relating to rendering a decision involving a potentially insurable applicant, said data being stored in a plurality of formats;   a computer executing a process for extracting at least a portion of the stored data and transforming the extracted data from the plurality of formats into a standardized format;
 wherein the memory further stores the transformed data in the standardized format; 
 wherein the computer further executes a heuristic engine for analyzing the transformed data for relevancy to a decision to be rendered involving the potentially insurable applicant and assigning one or more relevancy factors to the analyzed data as a function of the decision to be rendered, said relevancy factors providing an improved user experience by enabling adjustment by the computer to presenting the transformed data to an underwriter as a function of the decision to be rendered; and 
   a display displaying an output including the assigned relevancy factors to the underwriter for use in rendering the decision and further displaying, based on an adjustment by the computer, an updated output to the underwriter.   
     
     
         17 . The system of  claim 16 , wherein the data stored in the memory area 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, social network data, motor vehicle records data, other non-medical sources data, and datamart data. 
     
     
         18 . The system of  claim 16 , further wherein the heuristic engine executes at least one of: ant colony optimization, a heuristic algorithm, network theory, predictive modeling, deterministic chaos, behavioral economics, fractal geometry, and cellular automata.