GENERATING CREDIT BUILDING RECOMMENDATIONS THROUGH MACHINE LEARNING ANALYSIS OF USER ACTIVITY-BASED FEEDBACK

A real-time activity recommendation system receives an input from a user device regarding a targeted financial goal, such as a target credit score. Using machine learning models to evaluate patterns of user activity that contribute positively towards the goal, and to evaluate the limitations and opp...

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Bibliographische Detailangaben
Hauptverfasser:	King, Ryan Alexander, Sloboda, Tilo, Smith, Zachary Vaughn, Ducker, Michael
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:	A real-time activity recommendation system receives an input from a user device regarding a targeted financial goal, such as a target credit score. Using machine learning models to evaluate patterns of user activity that contribute positively towards the goal, and to evaluate the limitations and opportunities of the user's financial circumstances and profile, the recommendation system makes an assessment in real time to determine user actions that can be taken to improve credit health based on a user's profile and activity data. A user-specific recommendation regarding an activity that should be performed to reach the goal is generated and transmitted to the user. User and third party activity is later monitored as the user's financial status changes over time, and the recommendations are updated accordingly.