How machine learning works for payment fraud detection and prevention (stripe.com)

Rising global online payment fraud—estimated at $41B in 2022 and projected to reach $48B in 2023—has pushed businesses to adopt machine learning (ML) as a core defense. The article explains how ML’s ability to learn from large, dynamic datasets enables real‑time fraud detection and prevention that reduces losses, protects customer trust, and helps satisfy regulators. It outlines ML fundamentals (supervised, unsupervised, reinforcement learning) and argues ML is especially valuable because models can be retrained as attacker tactics evolve. Technically, common ML approaches include anomaly detection and risk scoring to flag suspicious transactions, graph/network analysis to expose fraud rings, text analysis for scam content, and identity verification via image/facial checks. Practical payment use cases range from POS and card‑present fraud detection to device fingerprinting and behavioral biometrics for mobile payments, plus account‑takeover, friendly fraud, invoice and loyalty‑program abuse detection. Operationally, successful systems rely on careful data preparation and feature engineering, model evaluation with metrics like precision/recall/F1, versioning and monitoring in production, and adaptive retraining to limit false positives. The article also highlights ethical and regulatory considerations—privacy, fairness and explainability—and notes that specialized certifications can help practitioners bridge ML techniques and real‑world fraud prevention.