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Bibliographic Details
Main Author:	huang, hai
Format:	Recurso digital
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Published:	Zenodo 2025
Online Access:	https://doi.org/10.5281/zenodo.17465720
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This project, titled “ZhiDun ZDC: An Intelligent Abnormal Transaction Detection and Risk Warning Platform Based on Dynamic Graph Neural Networks,” focuses on applying advanced graph learning techniques to anti–money laundering (AML) in the financial sector. The study proposes a Dynamic Graph Neural Network (Dynamic GNN) framework that models banking transaction networks as evolving graphs. Unlike traditional rule-based systems, the model dynamically adjusts the information propagation weights between accounts based on anomaly scores, enabling it to focus on suspicious entities and transaction paths. A dual-modality self-supervised learning approach is designed to jointly reconstruct both the network structure and transaction attributes, allowing the system to detect anomalies without requiring labeled data. To handle large-scale financial graphs, the research introduces a hierarchical graph training strategy using the Metis partitioning algorithm combined with K-means++ sampling, achieving high scalability and efficiency. Experimental evaluations on multiple datasets—including the Elliptic++ financial transaction dataset—demonstrate that ZDC achieves superior performance (AUC ≈ 0.93) compared with existing graph-based anomaly detection methods. Beyond technical innovation, the project explores its practical application within commercial banking, tailoring the model to real-world scenarios such as cross-border payments, layered transfers, and suspicious fund flows. The system outputs interpretable “risk paths” and can be integrated into banks’ existing AML and compliance systems for real-time monitoring and decision support.

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