Determining base limit values for contacts based on inter-network user interactions

Abstract

The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that increase a base limit value for a contact based on a user interaction with a graphical user interface. Generally, the disclosed system provides a graphical user interface for display to a user that includes base limit increase elements to increase base limit values for contacts of the user. Based on user selection of a base limit increase element, the disclosed system can provide an increased base limit value to an account associated with the contact.

What is claimed is: 
     
         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.