Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation
Xiyang Hu Yan Huang Beibei Li Tian Lu
FinTech lending (e.g., micro-lending) has played a significant role in
facilitating financial inclusion. It has reduced processing times and costs,
enhanced the user experience, and made it possible for people to obtain loans
who may not have qualified for credit from traditional lenders. However, there
are concerns about the potentially biased algorithmic decision-making during
loan screening. Machine learning algorithms used to evaluate credit quality can
be influenced by representation bias in the training data, as we only have
access to the default outcome labels of approved loan applications, for which
the borrowers' socioeconomic characteristics are better than those of rejected
ones. In this case, the model trained on the labeled data performs well on the
historically approved population, but does not generalize well to borrowers of
low socioeconomic background. In this paper, we investigate the problem of
representation bias in loan screening for a real-world FinTech lending
platform. We propose a new Transformer-based sequential loan screening model
with self-supervised contrastive learning and domain adaptation to tackle this
challenging issue. We use contrastive learning to train our feature extractor
on unapproved (unlabeled) loan applications and use domain adaptation to
generalize the performance of our label predictor. We demonstrate the
effectiveness of our model through extensive experimentation in the real-world
micro-lending setting. Our results show that our model significantly promotes
the inclusiveness of funding decisions, while also improving loan screening
accuracy and profit by 7.10% and 8.95%, respectively. We also show that
incorporating the test data into contrastive learning and domain adaptation and
labeling a small ratio of test data can further boost model performance.