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.

我们提出了一种新颖的多模态主动方法来解决金融网络犯罪检测中的可解释性问题。我们利用深度学习模型从交易顺序、子图连接和叙述生成中提取关键表示，显著简化分析师的调查过程，并利用 LLM 生成传真来帮助分析师进一步理解交易和其元数据。