  The application of graph representation learning techniques to the area of
financial risk management (FRM) has attracted significant attention recently.
However, directly modeling transaction networks using graph neural models
remains challenging: Firstly, transaction networks are directed multigraphs by
nature, which could not be properly handled with most of the current
off-the-shelf graph neural networks (GNN). Secondly, a crucial problem in FRM
scenarios like anti-money laundering (AML) is to identify risky transactions
and is most naturally cast into an edge classification problem with rich
edge-level features, which are not fully exploited by the prevailing GNN design
that follows node-centric message passing protocols. In this paper, we present
a systematic investigation of design aspects of neural models over directed
multigraphs and develop a novel GNN protocol that overcomes the above
challenges via efficiently incorporating directional information, as well as
proposing an enhancement that targets edge-related tasks using a novel message
passing scheme over an extension of edge-to-node dual graph. A concrete GNN
architecture called GRANDE is derived using the proposed protocol, with several
further improvements and generalizations to temporal dynamic graphs. We apply
the GRANDE model to both a real-world anti-money laundering task and public
datasets. Experimental evaluations show the superiority of the proposed GRANDE
architecture over recent state-of-the-art models on dynamic graph modeling and
directed graph modeling.
