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

TL;DR

This paper introduces a real-time transaction monitoring system that combines graph neural networks and generative explanation models to detect suspicious banking transactions and provide regulatory-compliant justifications, enhancing transparency and auditability.

Contribution

It presents an integrated framework using dynamic transaction graphs, GNNs, and retrieval-augmented generation for explainable compliance in banking.

Findings

Achieved 98.2% F1-score in detecting suspicious transactions

Generated high-quality, regulatory-aligned explanations confirmed by experts

Demonstrated effectiveness on simulated financial data stream

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.