EMPOWERING CREDIT SCORING THROUGH REJECTAWARE MULTI-TASK NETWORKS

Abstract

In financial credit scoring, loan applications may be approved or rejected. We can
only observe default/non-default labels for approved samples but have no
observations for rejected samples, which leads to missing-not-at-random selection
bias. Machine learning models trained on such biased data are inevitably unreliable.
In this work, we find that the default/non-default classification task and the
rejection/approval classification task are highly correlated, according to both realworld
data study and theoretical analysis. Consequently, the learning of default/nondefault
can benefit from rejection/approval. Accordingly, we for the first time propose
to model the biased credit scoring data with Multi-Task Learning (MTL). Specifically,
we propose a novel Reject-aware Multi-Task Network (RMT-Net), which learns the
task weights that control the information sharing from the rejection/approval task to
the default/non-default task by a gating network based on rejection probabilities.
RMT-Net leverages the relation between the two tasks that the larger the rejection
probability, the more the default/non-default task needs to learn from the
rejection/approval task. Furthermore, we extend RMT-Net to RMT-Net++ for
modeling scenarios with multiple rejection/approval strategies. Extensive experiments
are conducted on several datasets, and strongly verifies the effectiveness of RMT-Net
on both approved and rejected samples. In addition, RMT-Net++ further improves
RMT-Net’s performances.