Exploring pseudo-labeling for reject inference

Abstract

Banks use algorithms to estimate the credit risk of loan applicants. However, we need to retrain these models. When retraining, we only know the label, meaning whether the applicant defaulted or not, for those accepted for the loan. Retraining only with the accepted will result in biased models and losses for the bank due to selection bias. To counteract this issue, we can infer the labels of those rejected. This is known as reject inference. In this thesis, we will pursue pseudo-labeling to do reject inference, which needs two models, the first to create the pseudo-labels for the rejected and the second to make the final predictions. We will create the pseudo-labels by training a lightGBM on the available data. Afterward, we will apply a logistic regression as the final model. We will compare the results against a baseline, setting all rejected to a category (default /not default). In addition, we will compare to a scenario where the rejection results from random decision-making, experiment five rejection rates, and see the effect of setting to default vs. not default. We found that doing lightGBM to infer the labels had a lower F1 score, AUC, and profit for the bank. As such, the bank should set all rejected to a category. Additionally, we found that setting all to default has a higher recall in the rejected population and higher profit. Moreover, a lower rejection rate increases profits.

Date of Award	25 Jan 2024
Original language	English
Awarding Institution	Universidade Católica Portuguesa
Supervisor	Susana Brandão (Supervisor)