更好的小额信贷决策
Towards a Better Microcredit Decision
摘要：
反推包括推断被拒绝案例的可能还款行为的技术。在本文中，我们在一个全新的角度，通过捕捉的顺序模式之间的互动贷款业务的多个阶段，以更好地利用潜在的因果关系的信用模型。具体来说，我们首先定义了3个阶段，在整个贷款过程中，包括授信（AR），提款申请（WS）和还款承诺（GB）的顺序依赖，并将它们集成到一个多任务架构。在阶段内部，建立阶段内多任务分类以满足不同的业务目标。然后设计了一个信息走廊来表达顺序依赖，通过层次化的注意力模块控制信息通道的内容和大小，利用客户与平台之间的交互信息，从前面的阶段。此外，半监督损失被引入到处理未观察到的情况。本文提出的多阶段交互序列（MSIS）方法简单有效，在国内某大型贷款平台的真实数据集上的实验结果表明，该方法能够弥补群体偏差，提高模型的泛化能力。
英文摘要：
Reject inference comprises techniques to infer the possible repayment behavior of rejected cases. In this paper, we model credit in a brand new view by capturing the sequential pattern of interactions among multiple stages of loan business to make better use of the underlying causal relationship. Specifically, we first define 3 stages with sequential dependence throughout the loan process including credit granting(AR), withdrawal application(WS) and repayment commitment(GB) and integrate them into a multi-task architecture. Inside stages, an intra-stage multi-task classification is built to meet different business goals. Then we design an Information Corridor to express sequential dependence, leveraging the interaction information between customer and platform from former stages via a hierarchical attention module controlling the content and size of the information channel. In addition, semi-supervised loss is introduced to deal with the unobserved instances. The proposed multi-stage interaction sequence(MSIS) method is simple yet effective and experimental results on a real data set from a top loan platform in China show the ability to remedy the population bias and improve model generalization ability.
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。
