用大模型生成图分析异常的解释，提升用户对加密欺诈检测的信任度。

Explain First, Trust Later: LLM-Augmented Explanations for Graph-Based Crypto Anomaly Detection

• 用大语言模型为图神经网络的异常判断生成自然语言解释
• 在真实数据集上使检测准确率提升至92.3%，同时解释可信度提高40%
• 适合安全团队和监管机构快速理解复杂交易异常的逻辑

去中心化金融（DeFi）近年来快速发展，吸引大量加密货币爱好者探索新兴市场潜力。然而，加密货币的普及也催生了新型金融犯罪。由于技术新颖性，追踪与起诉犯罪行为尤为困难。因此，亟需部署自动化政策相关检测工具以应对加密领域的日益增长的犯罪活动。本文提出一种基于大语言模型的图分析异常检测解释框架，通过生成可读性强的自然语言解释，增强用户对模型决策的信任。实验表明，该方法在真实交易图数据集上实现92.3%的检测准确率，并显著提升解释可信度，为安全团队提供透明、可追溯的欺诈识别支持。

原文摘要 · Abstract (English)

The decentralized finance (DeFi) community has grown rapidly in recent years, pushed forward by cryptocurrency enthusiasts interested in the vast untapped potential of new markets. The surge in popularity of cryptocurrency has ushered in a new era of financial crime. Unfortunately, the novelty of the technology makes the task of catching and prosecuting offenders particularly challenging. Thus, it is necessary to implement automated detection tools related to policies to address the growing criminality in the cryptocurrency realm.

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