In this episode, explore how modern fraud detection leverages both supervised and unsupervised AI/ML techniques to identify anomalies and hidden patterns in transactional data. Discover how Datamatics’ hybrid approach—using clustering, decision trees, autoencoders, and graph analytics—enables near real-time detection of known and emerging fraud schemes, minimizing false positives and enhancing accuracy. Learn why strong feature engineering, continuous model retraining, and feedback loops are essential to build a fraud-defensive system that evolves with changing threat landscapes and delivers reliable ROI across BFSI and enterprise needs

https://www.datamatics.com/resources/whitepapers/how-to-discern-patterns-from-transactional-data-for-fraud-detection