Enhancing Illicit Activity Detection using XAI: A Multimodal Graph-LLM Framework
This addresses the need for better explainability in deep learning models for financial cybercrime prevention, benefiting organizations and governments, though it appears incremental as it builds on existing methods.
The paper tackles the problem of explainable AI (XAI) in financial cybercrime detection by proposing a multimodal graph-LLM framework that integrates transaction sequencing, subgraph connectivity, and narrative generation to streamline analyst investigations, resulting in a state-of-the-art approach.
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.