用大模型分析金融图谱中的洗钱行为，自动生成解释。
Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs
- 从金融图谱中提取目标实体的k跳邻域，转为结构化文本
- 通过少量示例提示，模型能识别异常并给出合理理由
- 适合需要可解释性的反洗钱分析场景
洗钱行为涉及复杂且相互关联的主体，需对图结构数据进行推理分析。本文探索将大语言模型（LLM）作为推理引擎，处理从金融知识图谱中提取的局部子图。我们提出一种轻量级流程：围绕关注实体检索k-hop邻域，将其序列化为结构化文本，并通过少样本上下文学习提示LLM评估可疑性并生成解释。基于反映常见洗钱行为的合成反洗钱（AML）场景实验表明，LLM能够模拟分析师逻辑，识别风险信号并提供连贯说明。本研究虽具探索性，但展示了基于LLM的图推理在反洗钱领域的潜力，为可解释、语言驱动的金融犯罪分析奠定了基础。
原文摘要 · Abstract (English)
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.
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