  Financial cybercrime prevention is an increasing issue with many
organisations and governments. As deep learning models have progressed to
identify illicit activity on various financial and social networks, the
explainability behind the model decisions has been lacklustre with the
investigative analyst at the heart of any deep learning platform. In our paper,
we present a state-of-the-art, novel multimodal proactive approach to
addressing XAI in financial cybercrime detection.
  We leverage a triad of deep learning models designed to distill essential
representations from transaction sequencing, subgraph connectivity, and
narrative generation to significantly streamline the analyst's investigative
process. Our narrative generation proposal leverages LLM to ingest transaction
details and output contextual narrative for an analyst to understand a
transaction and its metadata much further.
