  In this paper, we present a novel approach to identify linked fraudulent
activities or actors sharing similar attributes, using Graph Convolution
Network (GCN). These linked fraudulent activities can be visualized as graphs
with abstract concepts like relationships and interactions, which makes GCNs an
ideal solution to identify the graph edges which serve as links between
fraudulent nodes. Traditional approaches like community detection require
strong links between fraudulent attempts like shared attributes to find
communities and the supervised solutions require large amount of training data
which may not be available in fraud scenarios and work best to provide binary
separation between fraudulent and non fraudulent activities. Our approach
overcomes the drawbacks of traditional methods as GCNs simply learn
similarities between fraudulent nodes to identify clusters of similar attempts
and require much smaller dataset to learn. We demonstrate our results on linked
accounts with both strong and weak links to identify fraud rings with high
confidence. Our results outperform label propagation community detection and
supervised GBTs algorithms in terms of solution quality and computation time.
