Identifying Linked Fraudulent Activities Using GraphConvolution Network
This addresses fraud detection for financial or security domains by improving accuracy and efficiency in identifying fraud rings, though it appears incremental as it applies an existing GCN method to a specific problem.
The paper tackles the problem of identifying linked fraudulent activities by using Graph Convolution Networks (GCNs) to learn similarities between nodes, overcoming limitations of traditional methods like community detection and supervised algorithms. The results show that their approach outperforms label propagation community detection and supervised GBTs in solution quality and computation time.
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.