用AI提升反洗钱效率，让金融系统更透明可靠
AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systems
- 结合图结构检索与生成模型，优化KYC流程中的尽职调查
- 实验显示该方法在多种场景下保持高准确率与相关性
- 适合关注金融合规、AI安全与可解释性的研究者和从业者
洗钱与金融欺诈严重威胁全球金融稳定，每年造成数万亿损失，监管难度大。本文综述人工智能在反洗钱（AML）中的应用，通过提升检测准确率、降低误报率、减轻人工调查负担，推动可持续发展。提出未来研究方向：联邦学习实现隐私保护协作、公平性与可解释性AI、强化学习构建自适应防御，以及人机协同可视化系统，确保下一代AML架构透明、可问责、鲁棒。最后，提出一种基于图检索增强生成（RAG Graph）的AI驱动KYC应用，融合生成模型以提升尽职调查（CDD/EDD）的效率、透明度与决策支持。实验表明，RAG-Graph在多种评估场景中表现出高忠实度与强相关性，显著提升合规流程效率与资源利用率。
原文摘要 · Abstract (English)
Money laundering and financial fraud remain major threats to global financial stability, costing trillions annually and challenging regulatory oversight. This paper reviews how artificial intelligence (AI) applications can modernize Anti-Money Laundering (AML) workflows by improving detection accuracy, lowering false-positive rates, and reducing the operational burden of manual investigations, thereby supporting more sustainable development. It further highlights future research directions including federated learning for privacy-preserving collaboration, fairness-aware and interpretable AI, reinforcement learning for adaptive defenses, and human-in-the-loop visualization systems to ensure that next-generation AML architectures remain transparent, accountable, and robust. In the final part, the paper proposes an AI-driven KYC application that integrates graph-based retrieval-augmented generation (RAG Graph) with generative models to enhance efficiency, transparency, and decision support in KYC processes related to money-laundering detection. Experimental results show that the RAG-Graph architecture delivers high faithfulness and strong answer relevancy across diverse evaluation settings, thereby enhancing the efficiency and transparency of KYC CDD/EDD workflows and contributing to more sustainable, resource-optimized compliance practices.
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