Navigating the Dynamics of Financial Embeddings over Time
This work provides insights into financial system dynamics for analysts, though it is incremental as it applies existing methods to a new domain.
The authors tackled the problem of capturing similarity-based patterns in dynamic financial transaction graphs by applying scalable Graph Representation Learning, and they associated shifts in the latent space with economic events like the Covid-19 pandemic to extract real-world insights.
Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated. In this labyrinth of interactions that are continuously updated, there exists a variety of similarity-based patterns that can provide insights into the dynamics of the financial system. With the current work, we propose the application of Graph Representation Learning in a scalable dynamic setting as a means of capturing these patterns in a meaningful and robust way. We proceed to perform a rigorous qualitative analysis of the latent trajectories to extract real world insights from the proposed representations and their evolution over time that is to our knowledge the first of its kind in the financial sector. Shifts in the latent space are associated with known economic events and in particular the impact of the recent Covid-19 pandemic to consumer patterns. Capturing such patterns indicates the value added to financial modeling through the incorporation of latent graph representations.