Scenario Gamification to Provide Improved Mortgage and Securitization

Abstract

A method for analyzing credit score scenarios includes operations of receiving user input from a primary user indicating selection of a future credit score, conducting analytics on a current credit score, the future credit score, account information of the primary user, and secondary variables, to generate instructions that, when implemented, modify the account information of the primary user resulting in modification of the current credit score to the future credit score, and responsive to receiving additional input from the primary user, initiating implementation of one or more of the instructions at least through transmission of a first instruction to destination. The secondary variables may be a result of analyses of anonymized data of second users, and in some instances, indicate that alteration of a first variable within the account information of the primary user will have a greater impact in modifying the current credit score to the future credit score.

1 . A method for analyzing credit score scenarios, comprising:
 generating a first user interface (UI) screen that includes:
 a graphical representation of a current credit score of a primary user; and 
 a UI element configured to receive a user input corresponding to an adjusting of the current credit score; 
   receiving the user input from the primary user indicating selection of a future credit score;   conducting analytics based on the current credit score, the future credit score,
 account information of the primary user, and secondary variables, to generate instructions that, when implemented, modify the account information of the primary user resulting in modification of the current credit score to the future credit score;
 wherein the secondary variables are a result of analyses of anonymized data of one or more secondary users; and 
 wherein the secondary variables either indicate: (i) that alteration of a first variable within the account information of the primary user will have a greater impact in modifying the current credit score to the future credit score, or (ii) a trend in user behavior that will impact modifying the current credit score to the future credit score; and 
 
   responsive to receiving additional input from the primary user, initiating implementation of one or more of the instructions at least through transmission of a first instruction to a destination.   
     
     
         2 . The method of  claim 1 , wherein conducting analytics includes retrieving training data that includes the secondary variables and training a machine learning model that is configured to generate the instructions that, when implemented, modify the account information of the primary user. 
     
     
         3 . The method of  claim 1 , wherein the secondary variables include a first credit score, a second credit score, and account information for a secondary user of the one or more secondary users. 
     
     
         4 . The method of  claim 1 , wherein the instructions that, when implemented, modify the account information of the primary user include a type of action to be taken, an order in which at least a subset of the instructions is to be implemented, and a timing associated with one or more of the instructions. 
     
     
         5 . The method of  claim 1 , further comprising:
 generating a second UI screen that includes:
 the instructions generated by conducting the analytics; and 
 a second UI element that is configured to receive a second user input accepting the instructions. 
   
     
     
         6 . The method of  claim 5 , wherein the second UI screen further includes an actionable element, and wherein receiving the additional input includes detecting selection of the actionable display element indicating acceptance of the instructions. 
     
     
         7 . The method of  claim 1 , wherein implementation of one or more of the instructions includes automated initiation of applying for a loan, and wherein disbursement of funds of the loan modify the account information of the primary user. 
     
     
         8 . The method of  claim 1 , wherein conducting the analytics further includes:
 performing a correlation of account information of the primary user to determine a debt utilization rate, wherein generating the instructions is based on the debt utilization rate.   
     
     
         9 . A system comprising:
 one or more logic modules stored within a non-transitory storage medium, the one or more logic modules, when executed by one or more processors, perform operations including:
 generating a first user interface (UI) screen that includes:
 a graphical representation of a current credit score of a primary user; and 
 a UI element configured to receive a user input corresponding to an adjusting of the current credit score; 
 
 receiving the user input from the primary user indicating selection of a future credit score; 
 conducting analytics based on the current credit score, the future credit score, account information of the primary user, and secondary variables, to generate instructions that, when implemented, modify the account information of the primary user resulting in modification of the current credit score to the future credit score;
 wherein the secondary variables are a result of analyses of anonymized data of one or more secondary users; and 
 wherein the secondary variables either indicate: (i) that alteration of a first variable within the account information of the primary user will have a greater impact in modifying the current credit score to the future credit score, or (ii) a trend in user behavior that will impact modifying the current credit score to the future credit score; and 
 
 responsive to receiving additional input from the primary user, initiating implementation of one or more of the instructions at least through transmission of a first instruction to a destination. 
   
     
     
         10 . The system of  claim 9 , wherein conducting analytics includes retrieving training data that includes the secondary variables and training a machine learning model that is configured to generate the instructions that, when implemented, modify the account information of the primary user. 
     
     
         11 . The system of  claim 9 , wherein the secondary variables include a first credit score, a second credit score, and account information for a secondary user of the one or more secondary users. 
     
     
         12 . The system of  claim 9 , wherein the instructions that, when implemented, modify the account information of the primary user include a type of action to be taken, an order in which at least a subset of the instructions is to be implemented, and a timing associated with one or more of the instructions. 
     
     
         13 . The system of  claim 9 , wherein the operations further include:
 generating a second UI screen that includes:
 the instructions generated by conducting the analytics; and 
 a second UI element that is configured to receive second user input accepting the instructions. 
   
     
     
         14 . The system of  claim 13 , wherein the second UI screen further includes an actionable element, and wherein receiving the additional input includes detecting selection of the actionable display element indicating acceptance of the instructions. 
     
     
         15 . A network device, comprising:
 one or more processors; and   memory communicatively coupled to the one or more processors, the memory comprises machine readable instructions that when executed by the one or more processors, cause the one or more processors to perform operations including:
 generating a first user interface (UI) screen that includes:
 a graphical representation of a current credit score of a primary user; and 
 a UI element configured to receive a user input corresponding to an adjusting of the current credit score; 
 
 receiving the user input from the primary user indicating selection of a future credit score; 
 conducting analytics based on the current credit score, the future credit score, account information of the primary user, and secondary variables, to generate instructions that, when implemented, modify the account information of the primary user resulting in modification of the current credit score to the future credit score,
 wherein the secondary variables are a result of analyses of anonymized data of second users, and 
 wherein the secondary variables either indicate: (i) that alteration of a first variable within the account information of the primary user will have a greater impact in modifying the current credit score to the future credit score, or (ii) a trend in user behavior that will impact modifying the current credit score to the future credit score; and 
 
 responsive to receiving additional input from the primary user, initiating implementation of one or more of the instructions at least through transmission of a first instruction to a destination. 
   
     
     
         16 . The network device of  claim 15 , wherein conducting analytics includes retrieving training data that includes the secondary variables and training a machine learning model that is configured to generate the instructions that, when implemented, modify the account information of the primary user. 
     
     
         17 . The network device of  claim 15 , wherein the secondary variables include a first credit score, a second credit score, and account information for a secondary user of the one or more secondary users. 
     
     
         18 . The network device of  claim 15 , wherein the instructions that, when implemented, modify the account information of the primary user include a type of action to be taken, an order in which at least a subset of the instructions is to be implemented, and a timing associated with one or more of the instructions. 
     
     
         19 . The network device of  claim 15 , wherein the operations further include:
 generating a second UI screen that includes:
 the instructions generated by conducting the analytics; and 
 a second UI element that is configured to receive a second user input accepting the instructions. 
   
     
     
         20 . The network device of  claim 15 , wherein the second UI screen further includes an actionable element, and wherein receiving the additional input includes detecting selection of the actionable display element indicating acceptance of the instructions.