  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.
