The Role Of Cybersecurity In Combating Terror Financing And Money Laundering: A Critical Review Of AI And Machine Learning Approaches

Abstract

The massive proliferation of financial crimes, especially money laundering (ML) and supporting terrorism (TF), is a critical challenge of financial security and stability around the globe. The growing complexity and cross-jurisdictionalism of the illicit world call out a transition to more robust, data-driven compliance systems, not the traditional rule-based systems (RBS). This is a critical review which assiduously examines the status quo with regards to the incorporation of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques in resolute cybersecurity systems in the context of Anti-Money Laundering (AML) and Counter-Terrorist Financing (CTF) research that has been published in the last 5 years (2020-2025). Based on the ten seminal studies analyzed in detail and with a methodological focus, three fundamental limitations of the system are outlined that include the lack of flexibility and scalability in real time, insufficient unstructured data management, and the inability to consistently adhere to cross-border regulations. To overcome such gaps, the review summarizes the state-of-the-art solutions, i.e., the Dynamic Graph Neural Networks (DGNNs), [1]Federated Learning (FL), and Explainable AI (XAI). The study gives a four-layered, Resilient Multi-Layered Hybrid AML/CTF System  that centers on the empirical validation of these combined systems, in which strong cybersecurity solutions are necessary to ensure model integrity to counter adversarial machine learning attacks.