Financial cybercrime is an increasing threat to the public, corporations, and government entities as billions of euros are stolen and laundered annually through financial networks. ML offers a method of defending against illicit activity, preventing loss of assets, and identifying cybercriminals. This project is established to (i) address the evolving threat landscape, (ii) introduce graph restructuring methods to improve graph neural network performance in transaction monitoring, (iii) integrate large language models as a method of explainable artificial intelligence, and (iv) perform topological data analysis on transaction graph structures to identify persistent features to enhance the robustness of transaction monitoring.