Enhancing Illicit Activity Detection using XAI: A Multimodal Graph-LLM Framework

TL;DR

This paper introduces a multimodal framework combining deep learning and large language models to improve explainability and efficiency in detecting financial cybercrimes, aiding analysts in investigation.

Contribution

It presents a novel multimodal approach integrating transaction data, graph connectivity, and narrative generation to enhance XAI in financial cybercrime detection.

Findings

Improved interpretability of cybercrime detection models

Streamlined investigative process for analysts

Effective use of LLM for contextual narrative generation

Abstract

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.