When fraud detection models flag only 2% of transactions as suspicious, the real criminals often slip through. In this episode, Lucas and Luna unpack how a data scientist at a major payments processor used graph analytics and network features to uncover a hidden fraud ring that had evaded every rule-based and machine learning model for 18 months. The key insight: instead of scoring individual transactions, they built a similarity graph connecting merchants by shared IP addresses, device fingerprints, and refund patterns. A single connected component revealed 47 dummy merchants all feeding into one shell company. The model caught it in week two of deployment, recovering an estimated $340 million in chargebacks over the next quarter. We talk about why classic fraud models miss organised rings, how graph features like PageRank and community detection can surface collusion, and why graph neural networks are becoming the new standard in fintech fraud detection.

#GraphAnalytics #FraudDetection #DataScience #MachineLearning #NetworkAnalysis #Fintech #GraphNeuralNetworks #PageRank #CommunityDetection #AnomalyDetection #PaymentsProcessing #ChargebackFraud #FeatureEngineering #OrganizedFraud #Technology #FexingoBusiness #BusinessPodcast #DataDriven

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