Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking

• URL: http://arxiv.org/abs/2506.01093v1
• Date: Sun, 01 Jun 2025 17:34:57 GMT
• Title: Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking
• Abstract summary: This paper presents a real-time transaction monitoring framework that integrates graph-based modeling, narrative field embedding, and generative explanation.<n>The system constructs dynamic transaction graphs, extracts structural and contextual features, and classifies suspicious behavior using a graph neural network.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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