Fraud Detection Machine Learning
Last updated: April 2026 Machine learning fraud detection uses supervised classifiers (XGBoost, LightGBM), unsupervised anomaly detection (Isolation Forest, autoencoders), and graph neural networks on transaction graphs to score events in under 100 ms — flagging payment, account, and identity fraud with precision–recall AUC well above static rule engines. In 2026 the working stack is Kafka or … Read more