DETERMINING BASE LIMIT VALUES FOR CONTACTS BASED ON INTER-NETWORK USER INTERACTIONS

1 . A computer-implemented method comprising:

utilizing a trained activity machine learning model to generate a first activity score from a first set of activity data corresponding to a user having a first user asset account;

utilizing the trained activity machine learning model to generate a second activity score from a second set of activity data corresponding to a contact having a second user asset account;

determining a first base limit value for the first user asset account from the first activity score utilizing a base limit value model, wherein the first base limit value comprises a first excess utilization buffer for the first user asset account;

determining a second base limit value for the second user asset account from the second activity score utilizing the base limit value model, wherein the second base limit value comprises a second excess utilization buffer for the second user asset account;

providing, for display within a graphical user interface of a computing device corresponding to the user, a base limit increase element for increasing the second base limit value corresponding to the contact;

in response to receiving an indication of a selection of the base limit increase element:

increasing the second base limit value to a third excess utilization buffer for the second user asset account of the contact; and

providing, for display within an additional graphical user interface of a computing device corresponding to the contact, an additional base limit increase element corresponding to the user for providing an increased base limit value for the first user asset account; and

in response to receiving an indication of a selection of the additional base limit increase element from the computing device corresponding to the contact, increasing the first base limit value to a fourth excess utilization buffer corresponding the first user asset account.

2 . The computer-implemented method of claim 1 , wherein the trained activity machine learning model comprises a plurality of trained machine learning models and further comprising:

utilizing a first trained activity machine learning model from the plurality of trained machine learning models based on features corresponding to the user; and

utilizing a second trained activity machine learning model from the plurality of trained machine learning models based on features corresponding to the contact.

3 . The computer-implemented method of claim 1 , wherein determining the first base limit value and the second base limit value comprises determining the first excess utilization buffer for the first user asset account that is different than the second excess utilization buffer for the second user asset account.

4 . The computer-implemented method of claim 1 , further comprising providing, for display within the additional graphical user interface of the computing device corresponding to the contact, an indication regarding the third excess utilization buffer for the second user asset account of the contact together with the additional base limit increase element corresponding to the user for providing the increased base limit value for the first user asset account.

5 . The computer-implemented method of claim 1 , further comprising in response to receiving the indication of the selection of the additional base limit increase element from the computing device corresponding to the contact:

increasing the first base limit value to the fourth excess utilization buffer corresponding the first user asset account; and

providing, for display within the graphical user interface of the computing device corresponding to the user, an indication regarding the fourth excess utilization buffer.

6 . The computer-implemented method of claim 1 , further comprising providing, for display within the graphical user interface of the computing device corresponding to the user, the base limit increase element for increasing the second base limit value corresponding to the contact and an additional base limit increase element for increasing a third base limit value corresponding to an additional contact.

7 . The computer-implemented method of claim 6 , further comprising in response to receiving an additional indication of an additional selection of the additional base limit increase element increasing the third base limit value.

8 . The computer-implemented method of claim 1 , further comprising increasing the second base limit value for the second user asset account associated with the contact from the third excess utilization buffer based on determining that the contact satisfies one or more base limit value conditions.

9 . A non-transitory computer-readable medium comprising instructions that, when executed by at least one processor, cause a computing device to:

utilize a trained activity machine learning model to generate a first activity score from a first set of activity data corresponding to a user having a first user asset account;

utilize the trained activity machine learning model to generate a second activity score from a second set of activity data corresponding to a contact having a second user asset account;

determine a first base limit value for the first user asset account from the first activity score utilizing a base limit value model, wherein the first base limit value comprises a first excess utilization buffer for the first user asset account;

determine a second base limit value for the second user asset account from the second activity score utilizing the base limit value model, wherein the second base limit value comprises a second excess utilization buffer for the second user asset account;

provide, for display within a graphical user interface of a computing device corresponding to the user, a base limit increase element for increasing the second base limit value corresponding to the contact;

in response to receiving an indication of a selection of the base limit increase element:

increase the second base limit value to a third excess utilization buffer for the second user asset account of the contact; and

provide, for display within an additional graphical user interface of a computing device corresponding to the contact, an additional base limit increase element corresponding to the user for providing an increased base limit value for the first user asset account; and

in response to receiving an indication of a selection of the additional base limit increase element from the computing device corresponding to the contact, increase the first base limit value to a fourth excess utilization buffer corresponding the first user asset account.

10 . The non-transitory computer-readable medium of claim 9 , wherein the trained activity machine learning model comprises a plurality of trained machine learning models and further comprising instructions that, when executed by the at least one processor, cause the computing device to:

utilize a first trained activity machine learning model from the plurality of trained machine learning models based on features corresponding to the user; and

utilizing a second trained activity machine learning model from the plurality of trained machine learning models based on features corresponding to the contact.

11 . The non-transitory computer-readable medium of claim 9 , further comprising instructions that, when executed by the at least one processor, cause the computing device to determine the first base limit value and the second base limit value by determining the first excess utilization buffer for the first user asset account that is different than the second excess utilization buffer for the second user asset account.

12 . The non-transitory computer-readable medium of claim 9 , further comprising instructions that, when executed by the at least one processor, cause the computing device to provide, for display within the additional graphical user interface of the computing device corresponding to the contact, an indication regarding the third excess utilization buffer for the second user asset account of the contact together with the additional base limit increase element corresponding to the user for providing the increased base limit value for the first user asset account.

13 . The non-transitory computer-readable medium of claim 9 , further comprising instructions that, when executed by the at least one processor, cause the computing device to provide, for display within the graphical user interface of the computing device corresponding to the user, the base limit increase element for increasing the second base limit value corresponding to the contact and an additional base limit increase element for increasing a third base limit value corresponding to an additional contact.

14 . The non-transitory computer-readable medium of claim 13 , further comprising instructions that, when executed by the at least one processor, cause the computing device to, in response to receiving an additional indication of an additional selection of the additional base limit increase element, increasing the third base limit value corresponding to the additional contact.

15 . A system comprising:

at least one processor; and

at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:

utilize a trained activity machine learning model to generate a first activity score from a first set of activity data corresponding to a user having a first user asset account;

utilize the trained activity machine learning model to generate a second activity score from a second set of activity data corresponding to a contact having a second user asset account;

determine a first base limit value for the first user asset account from the first activity score utilizing a base limit value model, wherein the first base limit value comprises a first excess utilization buffer for the first user asset account;

determine a second base limit value for the second user asset account from the second activity score utilizing the base limit value model, wherein the second base limit value comprises a second excess utilization buffer for the second user asset account;

provide, for display within a graphical user interface of a computing device corresponding to the user, a base limit increase element for increasing the second base limit value corresponding to the contact;

in response to receiving an indication of a selection of the base limit increase element:

increase the second base limit value to a third excess utilization buffer for the second user asset account of the contact; and

provide, for display within an additional graphical user interface of a computing device corresponding to the contact, an additional base limit increase element corresponding to the user for providing an increased base limit value for the first user asset account; and

in response to receiving an indication of a selection of the additional base limit increase element from the computing device corresponding to the contact, increase the first base limit value to a fourth excess utilization buffer corresponding the first user asset account.

16 . The system of claim 15 , wherein the trained activity machine learning model comprises a plurality of trained machine learning models and further comprising instructions that, when executed by the at least one processor, cause the system to:

utilize a first trained activity machine learning model from the plurality of trained machine learning models based on features corresponding to the user; and

utilize a second trained activity machine learning model from the plurality of trained machine learning models based on features corresponding to the contact.

17 . The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to determine the first base limit value and the second base limit value by determining the first excess utilization buffer for the first user asset account that is different than the second excess utilization buffer for the second user asset account.

18 . The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to provide, for display within the additional graphical user interface of the computing device corresponding to the contact, an indication regarding the third excess utilization buffer for the second user asset account of the contact together with the additional base limit increase element corresponding to the user for providing the increased base limit value for the first user asset account.

19 . The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to:

provide, for display within the graphical user interface of the computing device corresponding to the user, the base limit increase element for increasing the second base limit value corresponding to the contact and an additional base limit increase element for increasing a third base limit value corresponding to an additional contact; and

in response to receiving an additional indication of an additional selection of the additional base limit increase element, increasing the third base limit value corresponding to the additional contact.

20 . The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to increase the second base limit value for the second user asset account associated with the contact from the third excess utilization buffer based on determining that the contact satisfies one or more base limit value conditions.