用图模型+生成AI实时监控银行交易并自动生成合规解释
Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking
- 构建动态交易图，用图神经网络识别可疑行为
- 98.2%的F1分数，97.8%精确率，97.0%召回率
- 生成符合监管条款的自然语言解释，适合审计场景
本文提出一种实时交易监控框架，融合图建模、叙事字段嵌入与生成式解释，支持自动化金融合规。系统构建动态交易图，提取结构与上下文特征，通过图神经网络分类可疑行为。采用检索增强生成模块，为每笔标记交易生成符合监管条文的自然语言解释。在模拟金融数据流上的实验显示，该方法取得98.2% F1分数、97.8%精确率和97.0%召回率。专家评估确认生成解释的质量与可解释性。结果表明，图智能与生成模型结合可支持高风险金融环境下的可解释、可审计合规。
原文摘要 · Abstract (English)
This paper presents a real-time transaction monitoring framework that integrates graph-based modeling, narrative field embedding, and generative explanation to support automated financial compliance. The system constructs dynamic transaction graphs, extracts structural and contextual features, and classifies suspicious behavior using a graph neural network. A retrieval-augmented generation module generates natural language explanations aligned with regulatory clauses for each flagged transaction. Experiments conducted on a simulated stream of financial data show that the proposed method achieves superior results, with 98.2% F1-score, 97.8% precision, and 97.0% recall. Expert evaluation further confirms the quality and interpretability of generated justifications. The findings demonstrate the potential of combining graph intelligence and generative models to support explainable, audit-ready compliance in high-risk financial environments.
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