US 12,339,926 B1
Systems and methods for risk factor predictive modeling with dynamic training
Marc Maier, Springfield, MA (US); and Sara Saperstein, Springfield, MA (US)
Assigned to Massachusetts Mutual Life Insurance Company, Springfield, MA (US)
Filed by MASSACHUSETTS MUTUAL LIFE INSURANCE COMPANY, Springfield, MA (US)
Filed on Jun. 1, 2021, as Appl. No. 17/336,036.
Claims priority of provisional application 63/046,856, filed on Jul. 1, 2020.
Int. Cl. G06F 18/2113 (2023.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06V 10/75 (2022.01)
CPC G06F 18/2113 (2023.01) [G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06V 10/751 (2022.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method for training predictive machine learning models for mitigating drift by dynamically updating quantitative contributions of covariate data comprising:
accessing, by a processor, a training dataset comprising data points including a plurality of covariates of a predictive machine learning model, wherein the predictive machine learning model is configured to generate predictions for the plurality of covariates and a net classification based on quantitative contributions of the plurality of covariates, wherein the training dataset comprises a plurality of historical application records associated with time metrics failing within a time period;
selecting, by the processor, one of the plurality of covariates representing a health characteristic of the predictive machine learning model, wherein each covariate is associated with one or more indicators;
selecting, by the processor, an indicator associated with the selected covariate;
generating, by the processor, a historical data distribution for selected covariate over a range of the selected indicator by applying the predictive machine learning model to the training dataset;
determining, by the processor, a current data distribution for the selected covariate over the range of the selected indicator by applying the predictive machine learning model to a test dataset comprising data points associated with time metrics falling within a recent time period;
when a comparison of the current data distribution with the historical data distribution indicates a change in data patterns exceeding a predetermined threshold,
automatically updating, by the processor, one or more parameters of the predictive machine learning model by reassigning a quantitative contribution of the selected covariate; and
training, by the processor, the predictive machine learning model to generate the net classification with the reassigned quantitative contribution of the selected covariate using the training dataset of historical application records.