Proactive Risk Management in Financial Transactions: A Hybrid ML Approach

Author(s)	Mithun Kumar Pusukuri
Country	United States
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.
Keywords	Financial Fraud Detection, Machine Learning Framework, Real-time Analytics, Cybersecurity, Transaction Processing.
Field	Computer
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-31
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.33994
Short DOI	https://doi.org/g82gg2

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International Journal For Multidisciplinary Research