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@article{183765,
        author = {Sanjay Chandrakant Vichare},
        title = {Data-Driven Remediation in AML Investigations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {4401-4408},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183765},
        abstract = {As the global financial system evolves, so too do the methods of financial crime. In response, anti-money laundering (AML) frameworks must adapt—particularly in the critical, often-overlooked area of remediation. Data-driven remediation uses artificial intelligence, machine learning, natural language processing, and automated workflows to transform the traditionally manual, error-prone AML case resolution process into a faster, more accurate, and more accountable system. This review explores current advancements in data-driven remediation for AML investigations, comparing traditional workflows to AI-enabled systems, and highlighting improvements in efficiency, false positive reduction, SAR generation, and regulatory compliance. Experimental evaluations and case studies confirm that integrating feedback loops, explainable AI, and automated SAR drafting significantly enhances operational and regulatory performance. The paper concludes with future directions aimed at building more adaptive, ethical, and scalable remediation frameworks for modern financial institutions.},
        keywords = {AML remediation, data-driven compliance, suspicious activity reports, explainable AI, AI governance, financial crime prevention, machine learning, natural language processing, fraud detection, RegTech},
        month = {August},
        }