| Abstract: |
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. |