Authentication control based on previous actions

1. A computer-implemented method comprising:

receiving, by a server and from an application of a remote user device, user interaction information;

predicting, using a machine-learning model trained on previous interactions of other users with their applications on their user devices and with call centers and based on the received user interaction information, a likelihood of a user of the application to contact a call center with an inquiry from the user;

populating a call center cache based on a prediction that the user will contact the call center;

authenticating the user based on the prediction for the user;

receiving a result of a user interaction between the user and the call center;

storing, in an application cache of a server and with a time-to-live (TTL) value, first information for the application;

monitoring a data stream of incoming information;

detecting in the data stream, second information associated with user;

storing, based on the detecting the second information, the second information in the application cache of the server, wherein the second information overwrites the first information and refreshes the TTL value;

retraining the machine-learning model, wherein the retraining of the machine-learning model is based on:

the previous interactions of the other users with their applications on their user devices and with the call centers;

the received user interaction information; and

the result of the user interaction between the user and the call center; and

repopulating, based on the retraining of the machine-learning model, an updated prediction that the user will contact the call center, and the detection of the second information in the data stream, the call center cache.

2. The computer-implemented method of claim 1 , further comprising:

detecting third information in the data stream associated with the user, wherein the third information comprises information not currently stored in the application cache;

storing, based on the detecting the third information, the third information in the application cache, wherein the third information receives a second TTL value;

receiving a request, from the remote user device, for the second information and the third information; and

sending, to the remote user device based on the request, the second information and the third information.

3. The computer-implemented method of claim 1 , further comprising:

receiving, from the application, a request for third information associated with the user;

determining, based on the request, the application cache does not currently store the third information;

receiving, from a data store, the third information and a second TTL value;

storing, in the application cache, the third information and the second TTL value; and

sending, based on the request, the third information from the application cache, and to the application, the third information.

4. A computer-implemented method comprising:

receiving, by a server and from an application of a remote user device, user interaction information;

predicting, using a machine-learning model trained on previous interactions of other users with their applications on their user devices and with call centers and based on the received user interaction information, a likelihood of a user of the application, associated with the received user interaction information, to contact a call center with an inquiry from the user;

populating a call center cache based on a prediction that the user will contact the call center;

receiving a result of a user interaction between the user and the call center;

authenticating the user based on the prediction for the user;

receiving, by the server and from the application, a request for first information associated with the user;

storing, in an application cache of a server and with a first time-to-live (TTL), value, first information for the application;

monitoring a data stream of incoming information;

detecting second information in the data stream;

storing, based on the detecting the second information, the second information in the application cache of the server, wherein the second information overwrites the first information and refreshes the first TTL value;

retraining the machine-learning model, wherein the retraining of the machine-learning model is based on:

the previous interactions of the other users with their applications on their user devices and with the call centers;

the received user interaction information; and

the result of the user interaction between the user and the call center; and

repopulating, based on the retraining of the machine-learning model, an updated prediction that the user will contact the call center, and the detection of the second information in the data stream, the call center cache.

5. The computer-implemented method of claim 4 , the method further comprising:

receiving, by the server, a second request for third information associated with the user;

determining, by the application cache, that the third information, of the user, is not currently stored in the application cache;

sending, to a data store and based on a determination that the third information of the user is not currently stored in the application cache, a third request for the third information;

receiving, based on the third request and from the data store, the third information associated;

determining, based on the received third information, a second TTL value for the third information received from the data store;

storing, in the application cache, the third information and the second TTL value; and

sending, based on the second request and the third information received from the data store, the third information to the application.

6. The computer-implemented method of claim 5 ,

wherein the first TTL value and the second TTL value are the same.

7. The computer-implemented method of claim 5 ,

wherein the first TTL value and the second TTL value are different.

8. The computer-implemented method of claim 5 , further comprising:

receiving, by the application cache and for the user, a modification of the first TTL value associated with the first information from the data stream; and

determining, based on the modification of the first TTL value, a third TTL value,

wherein the storing the first information and the first TTL value comprises storing, in the application cache, the first information and the third TTL value, and

wherein the storing the second information overwrites the first information and refreshes the third TTL value.

9. The computer-implemented method of claim 4 , further comprising:

storing, in the application cache, third information and a second TTL value;

detecting, by the server and from the data stream, fourth information;

updating, based on the fourth information, the third information; and

storing, in the application cache and based on the updating, an updated second TTL value associated the updated third information.

10. The computer-implemented method of claim 9 ,

wherein the third information is associated with a current account balance of the user, and

wherein the fourth information comprises a change in the current account balance, and

wherein updating, based on the fourth information, the current account balance results in an updated account balance.

11. The computer-implemented method of claim 4 , further comprising:

determining, from a table, the first TTL value associated with the first information.

12. The computer-implemented method of claim 4 , further comprising:

detecting, by the server, third information in the data stream;

based on a detection of the third information the data stream, sending, to a data store, a second request for fourth information, associated with the user;

receiving, based on the second request and from the data store, the fourth information;

determining, based on the fourth information, a second TTL value for the fourth information received from the data store;

storing, in the application cache, the fourth information and the second TTL value;

receiving, from the application, a third request for the fourth information; and

sending, based on the third request and to the application, the third information and the fourth information.

13. The computer-implemented method of claim 12 , wherein the foruth information indicates:

a transaction associated with the user has been declined,

a change in behavior of the user, or

a change in an account balance of an account associated with the user.

14. The computer-implemented method of claim 12 , wherein the fourth information comprises a most recent transaction associated with an account of the user.

15. The computer-implemented method of claim 4 , further comprising:

determining, by the server and based on information in the data stream, updated second information is being received;

receiving, from the application, a second request for the second information;

determining whether the updated second information has been received in its entirety; and

sending, based on a determination that the entirety of the updated second information has been received and as the second information, the updated second information to the application.

16. An apparatus comprising:

one or more processors; and

memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

receive, by a server and from an application of a remote user device, user interaction information associated with a user of the user device;

predict, using a machine-learning model trained on previous interactions of other users with their applications on their user devices and with call centers and based on the received user interaction information, a likelihood of the user of the application to contact a call center with an inquiry from the user;

populate a call center cache based on a prediction that the user will contact the call center;

authenticate the user based on the prediction for the user;

receive a result of a user interaction between the user and the call center;

store, in an application cache of a server and with a time-to-live (TTL) value, first information for the application;

monitor a data stream of incoming information;

detect second information in the data stream;

store, based on the detection of the second information, the second information in the application cache of the server, wherein the second information overwrites the first information and refreshes the TTL value;

retrain the machine-learning model, wherein the instruction to retrain of the machine-learning model are based on:

the previous interactions of the other users with their applications on their user devices and with the call centers;

the received user interaction information; and

the result of the user interaction between the user and the call center; and

repopulate, based on the retraining of the machine-learning model, based on an updated prediction that the user will contact the call center, and based on the detection of the second information in the data stream, the call center cache.

17. One or more non-transitory media storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps comprising:

receiving, by a server and from an application of a remote user device, user interaction information;

predicting, using a machine-learning model trained on previous interactions of other users with their applications on their user devices and with call centers and based on the received user interaction information, a likelihood of a user of the application to contact a call center with an inquiry from the user;

populating a call center cache based on a prediction that the user will contact the call center;

authenticating the user based on the prediction for the user;

receiving a result of a user interaction between the user and the call center;

receiving, from the application, a request for first information associated with the user;

storing, in an application cache of a server and with a time-to-live (TTL) value, first information for the application;

monitoring a data stream of incoming information;

detecting second information in the data stream;

storing, based on the detecting the second information, the second information in the application cache of the server, wherein the second information overwrites the first information and refreshes the TTL value;

receiving a result of an interaction between the call center and the user;

retraining machine-learning model, wherein the retraining of the machine-learning model is based on:

the previous interactions of the other users with their applications on their user devices and with the call centers;

the received user interaction information, and

the result of the user interaction between the user and the call center; and

repopulating, based on the retraining of the machine-learning model, an updated prediction that the user will contact the call center, and the detection of the second information in the data stream, the call center cache.