AI-Driven Anti-Money Laundering Systems
Abstract
Artificial Intelligence is transforming Anti-Money Laundering enforcement by enabling proactive detection, real-time surveillance, and data-driven decision-making across financial & criminal justice institutions.The present study examines how AI capabilities in anomaly detection, risk scoring, network analysis, and automated reporting are reshaping India's response to money laundering under the Prevention of Money Laundering Act.It analyses the integration of machine learning, natural language processing, and big data analytics into the investigative workflows of banks, the Financial Intelligence Unit of India, and enforcement agencies.Concerns regarding algorithmic transparency, bias, privacy, explainability, and the judicial admissibility of AI-generated evidence are critically evaluated.Through comparative insights from the United States, the United Kingdom, and Financial Action Task Force standards, the study proposes a framework for ethical, accountable, and technologically robust AI adoption that strengthens criminal justice outcomes while preserving constitutional safeguards.
