Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs
This is an incremental study exploring the potential of LLM-based graph reasoning for explainable financial crime analytics in anti-money laundering.
The paper tackled money laundering detection in financial graphs by using large language models (LLMs) for reasoning over localized subgraphs, showing that LLMs can emulate analyst-style logic and provide explanations in synthetic anti-money laundering scenarios.
The complexity and interconnectivity of entities involved in money laundering demand investigative reasoning over graph-structured data. This paper explores the use of large language models (LLMs) as reasoning engines over localized subgraphs extracted from a financial knowledge graph. We propose a lightweight pipeline that retrieves k-hop neighborhoods around entities of interest, serializes them into structured text, and prompts an LLM via few-shot in-context learning to assess suspiciousness and generate justifications. Using synthetic anti-money laundering (AML) scenarios that reflect common laundering behaviors, we show that LLMs can emulate analyst-style logic, highlight red flags, and provide coherent explanations. While this study is exploratory, it illustrates the potential of LLM-based graph reasoning in AML and lays groundwork for explainable, language-driven financial crime analytics.