Cognitive and heuristics-based emergent financial management

1. A system comprising:

a processor; and

a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:

receiving user input comprising one or more questions posed by a user and processed using natural language text processing;

receiving financial data and user data, the user data related to the one or more questions posed by the user and the financial data related to one or more of an individual, an organization, a process, or combinations thereof, wherein the financial data includes one or more of an interest rate and macroeconomic financial information;

creating data sets from the user data and the financial data based on determined classifications of the user data and the financial data; and

establishing relationships between the data sets;

wherein creating the data sets and establishing the relationships between the data sets comprises:

determining how the user data related to the one or more questions posed by the user and the financial data should be organized in comparison to other financial data by comparing past financial data, current financial data, and results of estimated financial data, wherein the results of the estimated financial data are based on a feedback loop;

employing, by a machine learning and reasoning component, classifiers comprising explicitly trained classifiers, implicitly trained classifiers or a combination of explicitly and implicitly trained classifiers; and

determining a conclusion based on the relationships, wherein the conclusion comprises performing a test process on a hypothesis.

2. The system of claim 1 , wherein the memory further stores executable instructions that, when executed by the processor, facilitate developing a personal management strategy for a customer based on the conclusion.

3. The system of claim 1 , wherein the user data and the financial data are in one or more of a structured format, a semi-structured format, an unstructured format, or combinations thereof.

4. The system of claim 1 , wherein the user data and the financial data are acquired from an internal source or an external source.

5. The system of claim 1 , wherein the user data and the financial data are acquired from an internal source and an external source.

6. The system of claim 1 , wherein creating data sets is further based on a capacity architecture that organizes the user data and the financial data based at least in part on capability of functions related to the user data and the financial data.

7. The system of claim 6 , wherein the capacity architecture is comprised of a cognitive and heuristic based emerging financial management section, a personal asset tracking section and a capability architecture knowledge base for an enterprise section.

8. The system of claim 7 , wherein the capability architecture knowledge base for an enterprise section comprises a data organization component that cross-references processes, technology and a stimulus-organism-response mapping.

9. A method comprising:

receiving user input comprising one or more questions posed by a user and processed using natural language text processing;

creating data sets from user data related to the one or more questions posed by the user and financial data based on determined classifications of the financial data wherein the financial data includes one or more of an interest rate and macroeconomic financial information;

generating relationships between the data sets;

wherein creating the data sets and generating the relationships between the data sets comprises:

determining data organization of the user data related to the one or more questions posed by the user and the financial data by comparing past financial data, current financial data, and results of estimated financial data, wherein the results of the estimated financial data are based on a feedback loop;

employing, by a machine learning and reasoning component, classifiers comprising explicitly trained classifiers, implicitly trained classifiers or a combination of explicitly and implicitly trained classifiers; and

determining a conclusion based on the relationships, wherein the conclusion comprises performing a test process on a hypothesis.

10. The method of claim 9 , wherein determining the conclusion is based in part on a feedback loop that represents previous recommendations.

11. The method of claim 9 , wherein creating data sets is further based on a capacity architecture that organizes the user data based at least in part on capability of functions related to the user data.

12. The method of claim 11 , wherein the capacity architecture is comprised of a cognitive and heuristic based emerging financial management section, a personal asset tracking section and a capability architecture knowledge base for an enterprise section.

13. The method of claim 12 , wherein the capability architecture knowledge base for an enterprise section comprises a data organization component that cross-references processes, technology and a stimulus-organism-response mapping.

14. A non-transitory computer-readable storage device that stores executable instructions that, in response to execution, cause a system comprising a processor to perform operations, comprising:

receiving user input comprising one or more questions posed by a user and processed using natural language text processing;

receiving financial data and user data, the user data related to the one or more questions posed by the user and the financial data related to one or more of an individual, an organization, a process, or combinations thereof, wherein the financial data includes one or more of an interest rate and macroeconomic financial information;

creating data sets from the user data and the financial data based on determined classifications of the user data and the financial data;

establishing relationships between the data sets;

wherein creating the data sets and establishing the relationships between the data sets comprises:

determining how the user data related to the one or more questions posed by the user and the financial data should be organized in comparison to other financial data by comparing past financial data, current financial data, and results of estimated financial data wherein the results of the estimated financial data are based on a feedback loop;

employing, by a machine learning and reasoning component, classifiers comprising explicitly trained classifiers, implicitly trained classifiers or a combination of explicitly and implicitly trained classifiers; and

developing a personal management strategy for a customer based on determined conclusions based on the relationships, wherein the conclusion comprises performing a test process on a hypothesis.

15. The non-transitory computer-readable storage device of claim 14 wherein creating data sets is further based on a capacity architecture that organizes data and the financial data based at least in part on capability of functions related to the user data and the financial data.

16. The non-transitory computer-readable storage device of claim 15 ,

wherein establishing relationships between the data sets is based at least in part on cross-referencing processes, technology and a stimulus-organism-response mapping of the capability of functions related to the user data and the financial data.

17. The non-transitory computer-readable storage device of claim 14 ,

wherein the classifiers employed are employed across different sections of a capacity architecture that is comprised of a cognitive and heuristic based emerging financial management section, a personal asset tracking section and a capability architecture knowledge base for an enterprise section.