Abstract: In some aspects, the techniques described herein relate to a method including: generating, by a computer program including one or more machine learning models, for an input record, a predicted amount of deposit assets, wherein the predicted amount of deposit assets is for an individual or household associated with the input record; transforming, with a mathematical transformation, the predicted amount of deposit assets to match a corresponding percentile range defined in a publicly available household asset survey or benchmark; determining a final estimate for the predicted amount of deposit assets, wherein the final estimate is determined to be in proportion with an estimated total of individual or household deposits.

Abstract: According to an embodiment of the present invention, a computer implemented method and system for isolating variables in a behavior prediction model comprises: identifying a plurality of groups comprising a first group of variables and a second group of variables; building a model, using a computer processor, for capturing an effect of the first group of variables in predicting behavior for customers; building a subsequent stage of the model, using a computer processor, on a second group of variables to neutralize the effect of the first group of variables; displaying results of the model wherein the results minimize the effect of the first group of variables in predicting behavior at a user interface; and identifying a response based on the results for a segment of the customers.

Abstract: According to an embodiment of the present invention, a computer implemented method and system for isolating variables in a behavior prediction model comprises: identifying a plurality of groups comprising a first group of variables and a second group of variables; building a model, using a computer processor, for capturing an effect of the first group of variables in predicting behavior for customers; building a subsequent stage of the model, using a computer processor, on a second group of variables to neutralize the effect of the first group of variables; displaying results of the model wherein the results minimize the effect of the first group of variables in predicting behavior at a user interface; and identifying a response based on the results for a segment of the customers.