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

arXiv: https://arxiv.org/abs/2506.01093 · HTML: https://mlsanity.com/paper/2506.01093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 1 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Applicability of Large Language Models and Generative Models for Legal Case Judgement Summarization](https://mlsanity.com/paper/2407.12848/) (2024)
- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [Finding Money Launderers Using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.13499/) (2023)
- [Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection](https://mlsanity.com/paper/2402.08593/) (2024)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Model Risk Management for Generative AI In Financial Institutions](https://mlsanity.com/paper/2503.15668/) (2025)
- [Boardwalk Empire: How Generative AI is Revolutionizing Economic Paradigms](https://mlsanity.com/paper/2410.15212/) (2024)

## Built on by (most cited first, 1 of 1)

- [AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systems](https://mlsanity.com/paper/2512.06240/) (2025)

## Abstract (arXiv)

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.