In this episode, explore how AI/ML techniques—ranging from supervised and unsupervised learning to hybrid models—can detect fraud in transactional data by uncovering hidden patterns, anomalies, and collusion. Learn about methods like clustering to spot outliers; behavior-based profiling; deep learning autoencoders for anomaly reconstruction; and graph analytics for revealing fraud rings. Discover why a hybrid supervised–unsupervised approach is key to real-time fraud defense across domains. (blog.datamatics.com)https://blog.datamatics.com/how-to-discern-patterns-from-transactional-data-for-fraud-detection

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