Financial Default Prediction via Motif-preserving Graph Neural Network
  with Curriculum Learning
Daixin Wang Zhiqiang Zhang Yeyu Zhao Kai Huang Yulin Kang Jun Zhou
User financial default prediction plays a critical role in credit risk
forecasting and management. It aims at predicting the probability that the user
will fail to make the repayments in the future. Previous methods mainly extract
a set of user individual features regarding his own profiles and behaviors and
build a binary-classification model to make default predictions. However, these
methods cannot get satisfied results, especially for users with limited
information. Although recent efforts suggest that default prediction can be
improved by social relations, they fail to capture the higher-order topology
structure at the level of small subgraph patterns. In this paper, we fill in
this gap by proposing a motif-preserving Graph Neural Network with curriculum
learning (MotifGNN) to jointly learn the lower-order structures from the
original graph and higherorder structures from multi-view motif-based graphs
for financial default prediction. Specifically, to solve the problem of weak
connectivity in motif-based graphs, we design the motif-based gating mechanism.
It utilizes the information learned from the original graph with good
connectivity to strengthen the learning of the higher-order structure. And
considering that the motif patterns of different samples are highly unbalanced,
we propose a curriculum learning mechanism on the whole learning process to
more focus on the samples with uncommon motif distributions. Extensive
experiments on one public dataset and two industrial datasets all demonstrate
the effectiveness of our proposed method.