Data Scientists Are Using Graph Neural Networks for Fraud Detection
from The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations Hosted by Fexingo
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. #GraphNeuralNetworks #FraudDetection #MachineLearning #DataScience #FinancialServices #AntiMoneyLaundering #GNN #GraphML #MessagePassing #InductiveLearning #ClassImbalance #Explainability #ProductionML #Technology #DataSciencePodcast #FexingoBusiness #BusinessPodcast #ModelDeployment Keep every episode free: buymeacoffee.com/fexingo
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