This chapter focuses on the potential integration of graph databases and generative artificial intelligence (GenAI), such as large language models (LLMs), for enhancement of anti-money laundering (AML) detection efforts. The explored methodology is primarily conceptual, outlining a framework for how these technological advancements could be combined, rather than detailing currently deployed systems. The methodology involves focusing on the growing role of LLMs in regulatory compliance and graph databases in uncovering money laundering schemes, thus paving the way for supporting an agile and proactive AML infrastructure. Graph databases facilitate investigators to identify hidden relationships within a network of illicit entities, exposing hidden relationships across entities. By leveraging the capabilities of GenAI, organisations could potentially map these complex relationships between entities in real-time and draw actionable insights. Moreover, such an integration would allow AML practitioners to visualise, interpret, and disrupt illicit networks with unprecedented precision. The integration, while offering enormous opportunity, underscores the importance of addressing challenges including data biases, ethical considerations, and the need for interdisciplinary collaboration to fully utilise capabilities of technological innovations in combatting financial crime.