AI-Powered Behavioral Biometrics: Multi-Layered Anomaly Detection Framework for Real-time Payment Security

This article presents a framework for AI-driven anomaly detection in real-time payment ecosystems, addressing the growing challenges of fraud in increasingly digitized financial environments. The article details a multi-layered approach that integrates behavioral biometrics, transaction metadata analysis, and deep neural networks within a privacy-preserving federated learning architecture. By examining the evolution from traditional rule-based systems to advanced machine learning implementations, this article demonstrates how dynamic behavioral baselines, deep-fake voice detection, and tiered response mechanisms substantially enhance security while reducing customer friction. The framework's deployment at a financial institution provides empirical evidence of significant performance improvements across detection accuracy, processing speed, and false positive rates. Beyond immediate fraud prevention benefits, the study explores future research directions in explainable AI, adversarial training, and lightweight implementation architectures that could further transform financial ecosystem security and potentially expand financial inclusion globally.