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arXiv · 2507.14785

Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs

Erfan Pirmorad
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Abstract

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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Erfan Pirmorad. 2025-07-20. Exploring the In-Context Learning Capabilities of LLMs for Money Laundering Detection in Financial Graphs. https://doi.org/10.1109/icdmw69685.2025.00026

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