In this episode, Lucas and Luna dive into how graph neural networks (GNNs) are transforming fraud detection in financial systems. They explore a real case from a major European bank that deployed GNNs to catch synthetic identity fraud — a scheme that cost U.S. lenders an estimated $6 billion in 2025. Lucas breaks down why traditional machine learning models fail on relational fraud patterns, how GNNs exploit transaction graphs, and the surprising finding that adding just two hops of neighbor information improved recall by 40%. Luna asks the tough questions about computational cost and explainability, and they discuss practical tools like PyTorch Geometric and DGL. If you're a data scientist looking for a cutting-edge application of deep learning on graphs, this episode is for you.

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