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.

翻译：金融科技贷款（如小额贷款）在促进金融普惠方面发挥了重要作用。它缩短了处理时间与成本，提升了用户体验，并使那些可能不符合传统贷款机构信贷资格的人群能够获得贷款。然而，在贷款筛选过程中，算法决策的潜在偏差问题令人担忧。用于评估信用质量的机器学习算法可能受到训练数据中代表性偏差的影响，因为我们仅能获取已批准贷款申请的违约结果标签，而这些借款人的社会经济特征通常优于被拒绝的申请者。在这种情况下，基于标签数据训练的模型在历史获批人群上表现良好，但难以泛化至社会经济背景较低的借款群体。本文针对真实金融科技借贷平台中的贷款筛选代表性偏差问题展开研究。我们提出一种基于Transformer的序列贷款筛选模型，该模型融合了自监督对比学习与领域适应技术，以应对这一挑战性问题。我们利用对比学习在未获批（无标签）贷款申请上训练特征提取器，并通过领域适应提升标签预测器的泛化性能。通过在真实小额贷款场景中的广泛实验，我们验证了模型的有效性。结果表明，该模型显著提升了资金决策的包容性，同时将贷款筛选准确率和利润分别提高了7.10%和8.95%。此外，我们发现将测试数据纳入对比学习与领域适应过程，并对少量测试数据进行标注，可进一步优化模型性能。