Leveraging graph-based learning for credit card fraud detection: a comparative study of classical, deep learning and graph-based approaches

Credit card fraud results in staggering financial losses amounting to billions of dollars annually, impacting both merchants and consumers. In light of the escalating prevalence of digital crime and online fraud, it is important for organizations to implement robust and advanced technology to efficiently detect fraud and mitigate the issue. Contemporary solutions heavily rely on classical machine learning (ML) and deep learning (DL) methods to handle such tasks. While these methods have been effective in many aspects of fraud detection, they may not always be sufficient for credit card fraud detection as they aren’t adaptable to detect complex relationships when it comes to transactions. Fraudsters, for example, might set up many coordinated accounts to avoid triggering limitations on individual accounts. In the context of fraud detection, the ability of Graph Neural Networks (GNN’s) to aggregate information contained within the local neighbourhood of a transaction enables them to identify larger patterns that may be missed by just looking at a single transaction. In this research, we conduct a thorough analysis to evaluate the effectiveness of GNNs in improving fraud detection over classical ML and DL methods. We first build an heterogeneous graph architecture with the source, transaction, and destination as our nodes. Next, we leverage Relational Graph Convolutional Network (RGCN) to learn the representations of nodes in our graph and perform node classification on the transaction node. Our experimental results demonstrate that GNN’s outperform classical ML and DL methods.