Risk Assessment for Networked-guarantee Loans
Using High-order Graph Attention Representation
Abstract
Assessing and predicting the default risk of
networked-guarantee loans is critical for the commercial banks and financial regulatory authorities.
The guarantee relationships between the loan companies are usually modeled as directed networks.
Learning the informative low-dimensional representation of the networks is important for the default risk prediction of loan companies, even for
the assessment of systematic financial risk level. In
this paper, we propose a high-order graph attention
representation method (HGAR) to learn the embedding of guarantee networks. Because this financial
network is different from other complex networks,
such as social, language, or citation networks, we
set the binary roles of vertices and define high-order
adjacent measures based on financial domain characteristics. We design objective functions in addition to a graph attention layer to capture the importance of nodes. We implement a productive learning strategy and prove that the complexity is nearlinear with the number of edges, which could scale
to large datasets. Extensive experiments demonstrate the superiority of our model over state-of-theart method. We also evaluate the model in a realworld loan risk control system, and the results validate the effectiveness of our proposed approaches