  Financial frauds cause billions of losses annually and yet it lacks efficient
approaches in detecting frauds considering user profile and their behaviors
simultaneously in social network . A social network forms a graph structure
whilst Graph neural networks (GNN), a promising research domain in Deep
Learning, can seamlessly process non-Euclidean graph data . In financial fraud
detection, the modus operandi of criminals can be identified by analyzing user
profile and their behaviors such as transaction, loaning etc. as well as their
social connectivity. Currently, most GNNs are incapable of selecting important
neighbors since the neighbors' edge attributes (i.e., behaviors) are ignored.
In this paper, we propose a novel behavior information aggregation network
(BIAN) to combine the user behaviors with other user features. Different from
its close "relatives" such as Graph Attention Networks (GAT) and Graph
Transformer Networks (GTN), it aggregates neighbors based on neighboring edge
attribute distribution, namely, user behaviors in financial social network. The
experimental results on a real-world large-scale financial social network
dataset, DGraph, show that BIAN obtains the 10.2% gain in AUROC comparing with
the State-Of-The-Art models.
