The rapid expansion of digital financial ecosystems has significantly transformed the scale, speed, and complexity of financial transactions across the globe. While these technological advancements have enhanced efficiency within modern financial systems, they have simultaneously created new avenues for illicit financial activities, particularly money laundering and the concealment of unlawfully obtained assets. Within this context, the identification and tracing of proceeds of crime have emerged as a critical challenge for regulatory authorities and financial institutions. Traditional anti-money laundering (AML) mechanisms—primarily reliant on rule-based monitoring systems and manual compliance procedures—often struggle to detect sophisticated laundering techniques embedded within vast volumes of financial data. Consequently, there is an increasing need for advanced technological solutions capable of identifying complex and evolving patterns of financial misconduct. Artificial Intelligence (AI) has recently gained significant attention as a transformative tool in financial crime detection. Through the application of machine learning algorithms, anomaly detection models, and data-driven analytics, AI systems possess the capability to analyze large-scale financial datasets, identify unusual transactional patterns, and generate predictive insights that may indicate the presence of illicit financial flows. This study examines the potential of AI-driven systems in detecting financial transactions associated with proceeds of crime and evaluates how intelligent analytical frameworks can enhance the effectiveness of anti-money laundering enforcement mechanisms. The paper further situates this technological analysis within the regulatory framework governing money laundering in India, particularly under the Prevention of Money Laundering Act, 2002. By examining the legal understanding of "proceeds of crime" alongside emerging AI-based monitoring techniques, the research highlights the intersection between advanced computational technologies and financial regulatory enforcement. The study also explores the technical capabilities of artificial intelligence models in detecting suspicious financial behavior, while addressing key challenges associated with algorithmic transparency, data privacy, and regulatory compliance.
Samarth Pratap Singh (Wed,) studied this question.