Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913870126252032 |
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| author | Khanvilkar, Kunal Kommuru, Kranthi |
| author_facet | Khanvilkar, Kunal Kommuru, Kranthi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_01093 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking Khanvilkar, Kunal Kommuru, Kranthi Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning 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. |
| title | Regulatory Graphs and GenAI for Real-Time Transaction Monitoring and Compliance Explanation in Banking |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2506.01093 |