RMT-Net：金融信用评分中非随机缺失数据的拒绝感知多任务网络建模
RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring
摘要：
在金融信用评分中，贷款申请可能被批准或拒绝。我们只能观察到已批准样本的默认/非默认标签，但无法观察到拒绝样本，这导致了缺失非随机选择偏倚。在这种有偏见的数据上训练的机器学习模型不可避免地是不可靠的。在这项工作中，我们发现，默认/非默认分类任务和拒绝/批准分类任务是高度相关的，根据现实世界的数据研究和理论分析。因此，默认/非默认的学习可以从拒绝/批准中受益。因此，我们第一次提出用多任务学习（MTL）对有偏差的信用评分数据进行建模。具体来说，我们提出了一种新的拒绝感知多任务网络（RMT-Net），它学习的任务权重，控制信息共享从拒绝/批准任务的默认/非默认任务的门控网络的拒绝概率的基础上。RMT-Net利用两个任务之间的关系，拒绝概率越大，默认/非默认任务需要从拒绝/批准任务中学习的越多。此外，我们将RMT-Net扩展到RMT-Net++，用于对具有多个拒绝/批准策略的场景进行建模。在多个数据集上进行了大量的实验，有力地验证了RMT-Net在批准和拒绝样本上的有效性。此外，RMT-Net++进一步提高了RMT-Net的性能。
英文摘要：
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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
