Financial organisations often struggle to effectively leverage the information contained within transactional networks observed in their data. This is because most transactional networks are massive and are highly dynamic, evolving constantly over time. Existing graph-based representation learning approaches studied in financial literature struggle to adapt to these unique challenges. We demonstrate the application of GraphSAGE, an inductive representation learning algortihm, to a real-world transactional network and showcase how to effectively utilise this information-rich asset in the context of banking. We show how the inductive capabilties of GraphSAGE enable inference over unseen nodes, making it particularly useful for embedding dynamic networks. We overcome scalability challenges faced by other approaches by implementing a GPU-enabled sampling-based training algorithm. This paper also shares outcomes from exploratory analysis on the resulting embeddings revealing interpretable clusters aligned with customer attributes. Finally, we illustrate how the embeddings can be utilised in downstream classification tasks by using them in a money mule detection model, demonstrating that the embeddings help improve the prioritisation of high-risk accounts. This work serves as a blueprint for financial organisations to practically harness graph-based representation learning to gain actionable insights from their transactional networks.