Abstract: Systems and methods for augmenting data by performing reject inference are disclosed. In one embodiment, the disclosed process trains an auto-encoder based on a subset of known labeled rows (e.g., non-default loan applicants). The process then infers labels for unlabeled rows using the auto-encoder (e.g., label some rows as non-default and some as default). The process then trains a machine learning model based on the known labeled rows and the inferred labeled rows. Applicant data is then processed by this new machine learning model to determine if a loan applicant is likely to default. If the loan applicant is not likely to default, the loan applicant is funded. For example, the loan applicant may be mailed a physical working credit card. However, if the loan applicant is likely to default, the loan applicant is rejected. For example, the loan applicant may be mailed a physical adverse action letter.

Abstract: Systems and methods for model explanation are disclosed. In one embodiment, the disclosed process determines a score based on a scoring function and a plurality of values associated with a plurality of features of a denied credit applicant. (e.g., credit score of 550, no loans repaid, etc.). The process then determines a score of an approved credit applicant. (e.g., credit score of 750, 3 loans repaid, etc.). A next differential credit assignment associated with the current denied/approved pair is then calculated. If a convergence stopping criteria, (e.g., current accuracy>99% based on a statistical t-distribution) is not satisfied, the process repeats for a different approved credit applicant. When the convergence stopping criteria is satisfied, explanation information is generated. For example, the explanation information may include an adverse action reason code, fairness metric, disparate impact metric, human readable text, feature importance metric, credit value, and/or an importance rank.