Machine learning tax based credit score prediction

Abstract

A feature generator generates a feature data structure (FDS) by pre-processing a tax return of a user by extracting inherent features and generating derived features based on the tax return; and populating entries of the FDS based on the inherent and derived features. A prediction engine generates a prediction data structure (PDS) based on the FDS. The PDS includes a prediction that the user has a credit score above a first credit score threshold by applying the features to a machine learning model (MLM). The MLM is trained using first tax returns for first tax payers, each having a credit score above the first threshold, and second tax returns for second tax payers, each having a credit score below the first threshold. A task manager executes, based on the prediction, a task that requires the credit score of the user exceed a second credit score threshold less than the first threshold.