Methods and systems for physiologically informed account metrics utilizing artificial intelligence

1. A system for physiologically informed account metrics utilizing artificial intelligence, the system comprising a computing device, the computing device designed and configured to:

receive, from a remote device, an account inquiry, wherein the account inquiry identifies a particular user and an account operation related to the particular user;

identify a biological extraction related to the particular user, wherein the biological extraction further comprises at least an element of user physiological data;

calculate a user account profile utilizing the user biological extraction, wherein the user account profile contains at least an element of user behavior data and at least an element of user hazard data, wherein in hazard data describes a user's predisposition to monetary risk based on a user's biological extraction;

generate an account machine-learning model, wherein the account machine-learning model utilizes the user account profile as an input and outputs an account metric; and

determine a response to the account inquiry utilizing the account metric.

2. The system of claim 1 , wherein the computing device is further configured to authenticate the account inquiry, wherein authenticating the account inquiry further comprises:

transmitting, to the remote device, an authentication request;

receiving, from the remote device, an identifier of the particular user; and

validating the identifier of the particular user.

3. The system of claim 1 , wherein the computing device is further configured to:

receive a plurality of data entries, each data entry of the plurality of data entries containing at least an element of data pertaining to a previous user account operation;

classify, using an account classifier, the plurality of data entries to a behavior pattern, wherein the account classifier utilizes the previous user account operation as an input and outputs the behavior pattern by executing a classification algorithm; and

identify the element of user behavior data utilizing the behavior pattern.

4. The system of claim 1 , wherein the computing device is further configured to:

retrieve a second biological extraction related to the particular user;

receive hazard training data, wherein hazard training data further comprises a plurality of biological extractions and a plurality of hazard labels;

generate a hazard machine-learning model using the hazard training data, wherein the hazard machine-learning model utilizes the second biological extraction related to the particular user an input and outputs a hazard label; and

identify the element of user hazard data utilizing the hazard label.

5. The system of claim 1 , wherein the computing device is further configured to:

retrieve at least an element of user personal profile information; and

calculate the user account profile to contain the at least an element of user personal profile information.

6. The system of claim 1 , wherein the computing device is further configured to calculate the user account profile to contain a user account score, wherein the user account score further comprises an account history factor, an outstanding account factor, an account length factor, and an account type factor.

7. The system of claim 1 , wherein the computing device is further configured to:

receive account training data wherein the account training data further comprises a plurality of account profiles and a plurality of correlated account metrics; and

generate the account machine-learning model utilizing the account training data and a first machine-learning algorithm.

8. The system of claim 1 , wherein the computing device is further configured to:

calculate a user effective age wherein the user effective age is calculated from a user chronological age and a user biological extraction; and

determine the response to the account inquiry utilizing the account metric and the user effective age.

9. The system of claim 1 , wherein the computing device is further configured to determine that the account inquiry does not satisfy the account metric and deny the account inquiry.

10. The system of claim 1 , wherein the computing device is further configured to determine that the account inquiry satisfies the account metric and approve the account inquiry.

11. A method of physiologically informed account metrics utilizing artificial intelligence, the method comprising:

receiving, by a computing device, an account inquiry, wherein the account inquiry identifies a particular user and an account operation related to the particular user;

identifying by the computing device, a biological extraction related to the particular user, wherein the biological extraction further comprises at least an element of user physiological data;

calculating by the computing device, a user account profile utilizing the user biological extraction, wherein the user account profile contains at least an element of user behavior data and at least an element of user hazard data, wherein in hazard data describes a user's predisposition to monetary risk based on a user's biological extraction;

generating by the computing device, an account machine-learning model wherein the account machine-learning model utilizes the user account profile as an input and outputs an account metric; and

determining by the computing device, a response to the account inquiry utilizing the account metric.

12. The method of claim 11 , wherein receiving the account inquiry further comprises:

authenticating the account inquiry wherein authenticating the account inquiry further comprises:

transmitting, to the remote device, an authentication request;

receiving, from the remote device, an identifier of the particular user; and

validating the identifier of the particular user.

13. The method of claim 11 , wherein calculating the user account profile further comprises:

receiving a plurality of data entries, each data entry of the plurality of data entries containing at least an element of data pertaining to a previous user account operation;

classifying, using an account classifier, the plurality of data entries to a behavior pattern, wherein the account classifier utilizes the previous user account operation as an input and outputs the behavior pattern by executing a classification algorithm; and

identifying the element of user behavior data utilizing the output behavior pattern.

14. The method of claim 11 , wherein calculating the user account profile further comprises:

retrieving a second biological extraction related to the particular user;

receiving hazard training data wherein hazard training data further comprises a plurality of biological extractions and a plurality of hazard labels;

generating a hazard machine-learning model using the hazard training data, wherein the hazard machine-learning model utilizes the second biological extraction related to the particular user an input and outputs a hazard label; and

identifying the element of user hazard data utilizing the hazard label.

15. The method of claim 11 , wherein calculating the user account profile further comprises:

retrieving at least an element of user personal profile information; and

calculating the user account profile to contain the at least an element of user personal profile information.

16. The method of claim 11 , wherein calculating the user account profile further comprises calculating a user account profile to contain a user account score, wherein the user account score further comprises an account history factor, an outstanding account factor, an account length factor, and an account type factor.

17. The method of claim 11 , wherein generating the account machine-learning model further comprises:

receiving account training data wherein the account training data further comprises a plurality of account profiles and a plurality of correlated account metrics; and

generating the account machine-learning model utilizing the account training data and a first machine-learning algorithm.

18. The method of claim 11 , wherein determining the response to the account inquiry further comprises:

calculating a user effective age wherein the user effective age is calculated from a user chronological age and a user biological extraction; and

determining the response to the account inquiry utilizing the account metric and the user effective age.

19. The method of claim 11 , wherein determining the response to the account inquiry further comprises determining that the account inquiry does not satisfy the account metric and deny the account inquiry.

20. The method of claim 11 , wherein determining the response to the account inquiry further comprises determining that the account inquiry satisfies the account metric and approve the account inquiry.