Episode 46 of The Data Science Podcast with Fexingo explores how graph neural networks (GNNs) are transforming fraud detection in financial services. Lucas and Luna break down a real case: how a major European bank used GNNs to catch a money-laundering ring that rule-based systems missed. They discuss why traditional fraud models fail with relational data, how GNNs represent transactions as a graph of accounts and connections, and what it means for data scientists building production pipelines. Topics include message-passing layers, inductive vs transductive learning, and the challenge of class imbalance in fraud datasets. The hosts also touch on explainability trade-offs and whether GNNs are ready for real-time scoring. If you're a data scientist curious about graph-based machine learning beyond social network recommendations, this episode gives you a concrete, business-driven example to learn from.

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