AML Detection and Reporting with Intelligent Automation and Machine learning
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Abstract
This research paper delves into the intersection of intelligent automation and machine learning to bolster Anti-Money Laundering (AML) efforts. As financial crimes become increasingly sophisticated, traditional methods struggle to keep pace, necessitating innovative approaches. Leveraging advanced machine learning algorithms, the proposed system aims to enhance AML detection by identifying patterns, anomalies, and emerging trends in financial transactions. The integration of intelligent automation streamlines the reporting process, ensuring efficient and timely compliance with regulatory requirements. Key elements of the research include feature engineering, model optimization, and real-time monitoring, all contributing to a robust and adaptive AML framework. The study showcases the potential of combining artificial intelligence technologies to fortify financial institutions against the evolving landscape of illicit financial activities.
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