Abstract
This article presents an innovative hybrid machine-learning framework to enhance proactive risk management in financial transactions. The framework combines continuous feedback mechanisms with real-time observability capabilities to address the growing challenges of fraud detection in digital payments. The system achieves superior anomaly detection while maintaining minimal latency by integrating supervised and unsupervised learning techniques with uncertainty-based deep learning. The framework's adaptive learning capabilities and enhanced transparency features provide financial institutions with robust tools for combating emerging fraud patterns while improving operational efficiency. The solution significantly improves security measures and user authentication accuracy by implementing advanced behavioral biometrics and blockchain-based verification systems.
Published Version
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