| CPC G06Q 30/0203 (2013.01) | 19 Claims |
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1. A computing system operatively connected with a user device, the computing system comprising:
a memory device; and
a processing device operatively coupled to the memory device, wherein the processing device is configured to execute computer-readable program code to:
iteratively train, using training data comprising a personal data set of a plurality of first users, a predictive model incorporating a machine learning program, the personal data set, including a data entry regarding an assessment score determined with respect to each respective first user, the predictive model being trained to predict a financial health assessment score from survey data of the plurality of first users, the training of the predictive model including:
inserting the training data into an iterative training and testing loop to predict a target variable; and
repeatedly predicting the target variable during each iteration of the training and testing loop, wherein each iteration of the training and testing loop has differing weights applied to one or more nodes of the machine learning program, each of the differing weights being updated with each iteration of the training and testing loop to reduce error in predicting the target variable, which improves predictability of the target variable and functionality of the predictive model;
deploy the trained predictive model;
predict, using the trained predictive model and based on occurrence of a triggering condition associated with a change to a data entry of the personal data set of the second user where the change to the data entry of the personal data set is identified from survey data of the second user, a predicted assessment score with respect to a second user that is associated with the user device, the predicted assessment score being attributed to the second user upon performance of one or more future activities that would increase a current assessment score of the second user, the predicting the predicted assessment score including:
accessing, from one or more storage locations, a personal financial data set of the second user, the one or more storage locations being associated with a financial institution with which the second user has an account;
correlating, via the trained predictive model, the personal financial data set of the second user to the personal data set of at least one of the first users, the trained predictive model utilizing cluster analysis to identify the at least one of the first users from a subset of the plurality of first users that have a highest similarity to the personal financial data of the second user and discovering a correlation to imply causality for the predicted assessment score of the second user; and
transmit, to the user device, a report of the predicted assessment score, wherein the reporting of the predicted score includes reporting a change in value of the predicted assessment score occurring as a result of change in the data entry of the personal data of the second user.
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