Automatic asset selection and creation system and method

1. A method comprising:

receiving, at a deployment computer, a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer;

determining, by the deployment computer, a likelihood value for each model of a plurality of models stored in a database based on the established values, the inquiry values, and a historic decision value retrieved from a historic decision value database;

for each model of the plurality of models stored in the database, determining, by the deployment computer, if the model in the plurality of models is a suitable model based on the likelihood value;

generating, by the deployment computer, one or more client response messages comprising one or more assets created by one or more suitable models; and

transmitting, by the deployment computer, the one or more client response messages comprising the one or more assets to the client computer.

2. The method of claim 1 further comprising:

retrieving, by the deployment computer, the plurality of models from the database.

3. The method of claim 1 further comprising:

for each model of the plurality of models, comparing the likelihood value to a predetermined threshold; and

for each model of the plurality of models, determining if the model is suitable if the likelihood value exceeds the predetermined threshold.

4. The method of claim 3 , wherein the likelihood value is in a range of 0-100, and the predetermined threshold is in a range of 0-100.

5. The method of claim 1 , wherein the historic decision value is based on at least previously determined likelihood values.

6. The method of claim 1 further comprising:

determining, by the deployment computer, the one or more assets from an asset repository corresponding to the one or more suitable models; and

providing, by the deployment computer, access to the one or more assets to the client computer.

7. The method of claim 1 , wherein the plurality of models comprise a plurality of machine learning models.

8. The method of claim 1 , wherein at least one the plurality of models comprises a fraud determination model.

9. The method of claim 1 , wherein at least one of the plurality of models is based on a neural network.

10. A method of comprising:

receiving, at a deployment computer, a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer;

for each model of a plurality of models stored in a database, determining, by the deployment computer, if the model in the plurality of models is a suitable model based on a likelihood value;

generating, by the deployment computer, one or more client response messages comprising one or more assets created by one or more suitable models; and

transmitting, by the deployment computer, the one or more client response messages comprising the one or more assets to the client computer,

wherein the method further comprises:

determining, by the deployment computer, whether or not to create a new model based on the client request message;

after determining to create the new model, determining, by the deployment computer, a summation value based on the established values, the inquiry values, creation probability values, and additional data; and

storing, by the deployment computer, a model determined based on the summation value into the database.

11. A deployment computer comprising:

a processor;

a memory device; and

a computer-readable medium coupled to the processor, the computer-readable medium comprising code executable by the processor for implementing a method comprising:

receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer;

determining a likelihood value for each model of a plurality of models stored in a database based on the established values, the inquiry values, and a historic decision value retrieved from memory;

for each model of the plurality of models stored in the database, determining if the model in the plurality of models is a suitable model based on the likelihood value;

generating one or more client response messages comprising one or more assets created by one or more suitable models; and

transmitting the one or more client response messages comprising the one or more assets to the client computer.

12. The deployment computer of claim 11 , wherein the method further comprises:

retrieving, by the deployment computer, the plurality of models from the database.

13. The deployment computer of claim 11 , wherein the method further comprises:

for each model of the plurality of models, comparing the likelihood value to a predetermined threshold; and

for each model of the plurality of models, determining if the model is suitable if the likelihood value exceeds the predetermined threshold.

14. The deployment computer of claim 13 , wherein the likelihood value is in a range of 0-100, and the predetermined threshold is in a range of 0-100.

15. The deployment computer of claim 11 , wherein the historic decision value is based on at least previously determined likelihood values.

16. The deployment computer of claim 11 , wherein the method further comprises:

determining, by the deployment computer, the one or more assets from an asset repository corresponding to the one or more suitable models; and

providing, by the deployment computer, access to the one or more assets to the client computer.

17. A deployment computer comprising:

a processor;

a memory device; and

a computer-readable medium coupled to the processor, the computer-readable medium comprising code executable by the processor for implementing a method comprising:

receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer;

for each model of a plurality of models stored in a database, determining if the model in the plurality of models is a suitable model based on a likelihood value;

generating one or more client response messages comprising one or more assets created by one or more suitable models; and

transmitting the one or more client response messages comprising the one or more assets to the client computer, and wherein the method further comprises:

determining, by the deployment computer, whether or not to create a new model based on the client request message;

after determining to create the new model, determining, by the deployment computer, a summation value based on the established values, the inquiry values, creation probability values, and additional data; and

storing, by the deployment computer, a model determined based on the summation value into the database.

18. A deployment computer comprising:

a processor;

a memory device; and

a computer-readable medium operatively coupled to the processor, the computer-readable medium comprising code executable by the processor for implementing a method comprising:

receiving a client request message comprising a plurality of established parameters and established values for the established parameters, and inquiry parameters and inquiry values for the inquiry parameters from a client computer;

for each model of a plurality of models stored in a database, determining if the model in the plurality of models is a suitable model based on a likelihood value;

generating one or more client response messages comprising one or more assets created by one or more suitable models; and

transmitting the one or more client response messages comprising the one or more assets to the client computer, and wherein the computer medium further comprises:

a request service module;

a model determination module;

a model characterization module;

an execution module; and

an audit module.

19. A method comprising:

generating, by a client computer, a client request message comprising a plurality of established parameters and values for the established parameters, and inquiry parameters and values for the inquiry parameters;

transmitting, by the client computer, the client request message to a deployment computer, wherein the deployment computer, for each model of a plurality of models stored in a database, determines if a model in the plurality of models is a suitable model based on a likelihood value, and generates one or more client response messages comprising one or more assets created by one or more suitable models; and

receiving, by the client computer, the one or more client response messages from the deployment computer,

wherein the deployment computer further determines whether or not to create a new model based on the client request message, determines a summation value based on the values for the established parameters, the values for the inquiry parameters, creation probability values, and additional data, and stores the new model determined based on the summation value into the database.

20. The method of claim 19 , wherein the likelihood value is determined based on at least a historic decision value, wherein the historic decision value is associated with the client computer.