AI-Augmented AML Workflow Optimization In High-Volume Financial Institutions

Anti-Money Laundering (AML) compliance is a cornerstone of financial integrity, yet traditional rule-based workflows in high-volume financial institutions often struggle to balance false positives, operational overhead, and dynamic regulatory requirements. This paper presents a scalable, AI-augmented AML workflow architecture that integrates machine learning for adaptive risk scoring, natural language processing (NLP) for unstructured data ingestion, and robotic process automation (RPA) for case handling. We demonstrate that our approach improves detection accuracy, reduces alert fatigue, and shortens investigative timelines, enabling institutions to meet regulatory expectations efficiently while optimizing resource allocation. A comparative evaluation on synthetic and real-world datasets validates the system's precision, recall, and operational efficiency. The proposed framework is practical, scalable, and impactful for both enterprise deployment and supervisory oversight.