AI-Driven Reasoning Mechanism for Enhanced Detection of Illicit Money Flows

Abstract

Law enforcement agencies face substantial difficulties tracking illicit money flow activities because these operations have become more complex and difficult to detect. Conventional detection methods often struggle to reveal advancing criminal financial networks effectively. To address this gap, this study proposes an innovative AI-driven reasoning mechanism that leverages advanced natural language processing, deep learning, machine learning, and human crowd intelligence. The suggested methodology exclusively incorporates automated reasoning capabilities with insights from expert human input, creating a forceful framework capable of discovering subtle patterns indicative of money laundering. By using innovative artificial intelligence tools and stylometric analysis, the reasoning mechanism increases the transparency, interpretability, and reliability of investigative processes. This research contributes to anti-money laundering investigations by supporting law enforcement agencies with a sophisticated analytical system that can detect complex money laundering activities while staying efficient and scalable.