RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring. (arXiv:2206.00568v1 [cs.LG])

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 real-world data study and theoretical analysis.
Consequently, the learning of default/non-default can benefit from
rejection/approval. …

arxiv credit data financial modeling network random scoring