Skip to main navigation menu Skip to main content Skip to site footer

Vol. 5 No. 1 (2024)
2024 Edition

Articles

Dynamic Graph Neural Networks for Multi-Level Financial Fraud Detection: A Temporal-Structural Approach

Published 2024-09-19

  • Toan Khang Trinh
    • ,
  • Zhuxuanzi Wang

Toan Khang Trinh
Computer Science, California State University Long Beach, CA, USA

Zhuxuanzi Wang
Information Systems, Cornell Tech, NY, USA

Abstract

Financial fraud detection presents significant challenges due to the complex, dynamic, and multi-level nature of fraudulent activities in modern financial systems. This paper proposes a Dynamic Graph Neural Network (DGNN) framework that captures both temporal dynamics and structural patterns across transaction, account, and community levels for comprehensive fraud detection. The architecture integrates a multi-level financial network construction method with a temporal-structural feature extraction module and a hierarchical detection framework. The temporal-structural approach employs graph attention networks for capturing spatial relationships between financial entities while utilizing temporal convolution networks to model evolving patterns. Bidirectional message passing enables information flow between different network levels, allowing the detection of sophisticated fraud schemes that operate across multiple organizational scales. Extensive experiments on three real-world financial datasets (CCFraud, MPFraud, and BankNet) demonstrate that our approach consistently outperforms state-of-the-art methods, achieving average improvements of 1.4%, 5.7%, and 5.5% in AUC-ROC, AUC-PR, and F1-score respectively. Ablation studies confirm the significance of each component in the architecture, with the combination of temporal and structural features providing substantial performance gains. The model shows particular strength in detecting complex fraud patterns involving multiple accounts and extending over longer time periods, validating the effectiveness of our multi-level approach for financial fraud detection in dynamic environments.

Keywords

Graph Neural Networks, Financial Fraud Detection, Temporal-Structural Analysis, Multi-Level Architecture

View PDF

References

  1. Wang, M., Hu, X., & Du, Y. (2024, January). Enhancing recommender systems performance using knowledge graph embedding with graph neural networks. In 2024 4th International Conference on Neural Networks, Information and Communication (NNICE) (pp. 233-238). IEEE.
  2. Tiezzi, M., Ciravegna, G., & Gori, M. (2022). Graph neural networks for graph drawing. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 4668-4681.
  3. Kisanga, P., Woungang, I., Traore, I., & Carvalho, G. H. (2023, February). Network anomaly detection using a graph neural network. In 2023 International Conference on Computing, Networking and Communications (ICNC) (pp. 61-65). IEEE.
  4. Waikhom, L., & Patgiri, R. (2022, January). Recurrent convolution based graph neural network for node classification in graph structure data. In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 201-206). IEEE.
  5. Muppidi, S., Angadi, A., & Gorripati, S. K. (2022, March). Semi-supervised label propagation community detection on graphs with graph neural network. In 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) (pp. 1-6). IEEE.
  6. Zhao, Q., Chen, Y., & Liang, J. (2024). Attitudes and Usage Patterns of Educators Towards Large Language Models: Implications for Professional Development and Classroom Innovation. Academia Nexus Journal, 3(2).
  7. Zhang, J., Xiao, X., Ren, W., & Zhang, Y. (2024). Privacy-Preserving Feature Extraction for Medical Images Based on Fully Homomorphic Encryption. Journal of Advanced Computing Systems, 4(2), 15-28.
  8. Xiao, X., Chen, H., Zhang, Y., Ren, W., Xu, J., & Zhang, J. (2025). Anomalous Payment Behavior Detection and Risk Prediction for SMEs Based on LSTM-Attention Mechanism. Academic Journal of Sociology and Management, 3(2), 43-51.
  9. Xiao, X., Zhang, Y., Chen, H., Ren, W., Zhang, J., & Xu, J. (2025). A Differential Privacy-Based Mechanism for Preventing Data Leakage in Large Language Model Training. Academic Journal of Sociology and Management, 3(2), 33-42.
  10. Xu, K., & Purkayastha, B. (2024). Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment. Academic Journal of Sociology and Management, 2(6), 19-24.
  11. Xu, K., & Purkayastha, B. (2024). Enhancing Stock Price Prediction through Attention-BiLSTM and Investor Sentiment Analysis. Academic Journal of Sociology and Management, 2(6), 14-18.
  12. Shu, M., Liang, J., & Zhu, C. (2024). Automated Risk Factor Extraction from Unstructured Loan Documents: An NLP Approach to Credit Default Prediction. Artificial Intelligence and Machine Learning Review, 5(2), 10-24.
  13. Shu, M., Wang, Z., & Liang, J. (2024). Early Warning Indicators for Financial Market Anomalies: A Multi-Signal Integration Approach. Journal of Advanced Computing Systems, 4(9), 68-84.
  14. Liu, Y., Bi, W., & Fan, J. (2025). Semantic Network Analysis of Financial Regulatory Documents: Extracting Early Risk Warning Signals. Academic Journal of Sociology and Management, 3(2), 22-32.
  15. Zhang, Y., Fan, J., & Dong, B. (2025). Deep Learning-Based Analysis of Social Media Sentiment Impact on Cryptocurrency Market Microstructure. Academic Journal of Sociology and Management, 3(2), 13-21.
  16. Zhou, Z., Xi, Y., Xing, S., & Chen, Y. (2024). Cultural Bias Mitigation in Vision-Language Models for Digital Heritage Documentation: A Comparative Analysis of Debiasing Techniques. Artificial Intelligence and Machine Learning Review, 5(3), 28-40.
  17. Zhang, Y., Zhang, H., & Feng, E. (2024). Cost-Effective Data Lifecycle Management Strategies for Big Data in Hybrid Cloud Environments. Academia Nexus Journal, 3(2).
  18. Wu, Z., Feng, E., & Zhang, Z. (2024). Temporal-Contextual Behavioral Analytics for Proactive Cloud Security Threat Detection. Academia Nexus Journal, 3(2).
  19. Ji, Z., Hu, C., Jia, X., & Chen, Y. (2024). Research on Dynamic Optimization Strategy for Cross-platform Video Transmission Quality Based on Deep Learning. Artificial Intelligence and Machine Learning Review, 5(4), 69-82.
  20. Zhang, K., Xing, S., & Chen, Y. (2024). Research on Cross-Platform Digital Advertising User Behavior Analysis Framework Based on Federated Learning. Artificial Intelligence and Machine Learning Review, 5(3), 41-54.
  21. Xiao, X., Zhang, Y., Chen, H., Ren, W., Zhang, J., & Xu, J. (2025). A Differential Privacy-Based Mechanism for Preventing Data Leakage in Large Language Model Training. Academic Journal of Sociology and Management, 3(2), 33-42.
  22. Xiao, X., Chen, H., Zhang, Y., Ren, W., Xu, J., & Zhang, J. (2025). Anomalous Payment Behavior Detection and Risk Prediction for SMEs Based on LSTM-Attention Mechanism. Academic Journal of Sociology and Management, 3(2), 43-51.
  23. Liu, Y., Feng, E., & Xing, S. (2024). Dark Pool Information Leakage Detection through Natural Language Processing of Trader Communications. Journal of Advanced Computing Systems, 4(11), 42-55.
  24. Chen, Y., Zhang, Y., & Jia, X. (2024). Efficient Visual Content Analysis for Social Media Advertising Performance Assessment. Spectrum of Research, 4(2).
  25. Wu, Z., Wang, S., Ni, C., & Wu, J. (2024). Adaptive Traffic Signal Timing Optimization Using Deep Reinforcement Learning in Urban Networks. Artificial Intelligence and Machine Learning Review, 5(4), 55-68.
  26. Chen, J., & Zhang, Y. (2024). Deep Learning-Based Automated Bug Localization and Analysis in Chip Functional Verification. Annals of Applied Sciences, 5(1).
  27. Zhang, Y., Jia, G., & Fan, J. (2024). Transformer-Based Anomaly Detection in High-Frequency Trading Data: A Time-Sensitive Feature Extraction Approach. Annals of Applied Sciences, 5(1).
  28. Zhang, D., & Feng, E. (2024). Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning. Journal of Advanced Computing Systems, 4(10), 38-54.
  29. Ju, C., Jiang, X., Wu, J., & Ni, C. (2024). AI-Driven Vulnerability Assessment and Early Warning Mechanism for Semiconductor Supply Chain Resilience. Annals of Applied Sciences, 5(1).
  30. Wan, W., Guo, L., Qian, K., & Yan, L. (2025). Privacy-Preserving Industrial IoT Data Analysis Using Federated Learning in Multi-Cloud Environments. Applied and Computational Engineering, 141, 7-16.
  31. Wu, Z., Zhang, Z., Zhao, Q., & Yan, L. (2025). Privacy-Preserving Financial Transaction Pattern Recognition: A Differential Privacy Approach. Applied and Computational Engineering, 146, 30-40.
  32. Rao, G., Zheng, S., & Guo, L. (2025). Dynamic Reinforcement Learning for Suspicious Fund Flow Detection: A Multi-layer Transaction Network Approach with Adaptive Strategy Optimization. Applied and Computational Engineering, 145, 1-11.
  33. Yan, L., Weng, J., & Ma, D. (2025). Enhanced TransFormer-Based Algorithm for Key-Frame Action Recognition in Basketball Shooting.
  34. Wang, Y., Wan, W., Zhang, H., Chen, C., & Jia, G. (2025). Pedestrian Trajectory Intention Prediction in Autonomous Driving Scenarios Based on Spatio-temporal Attention Mechanism.
  35. Ni, X., Yan, L., Xiong, K., & Liu, Y. (2024). A Hierarchical Bayesian Market Mix Model with Causal Inference for Personalized Marketing Optimization. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 378-396.
  36. Yan, L., Zhou, S., Zheng, W., & Chen, J. (2024). Deep Reinforcement Learning-based Resource Adaptive Scheduling for Cloud Video Conferencing Systems.
  37. Rao, G., Lu, T., Yan, L., & Liu, Y. (2024). A Hybrid LSTM-KNN Framework for Detecting Market Microstructure Anomalies:: Evidence from High-Frequency Jump Behaviors in Credit Default Swap Markets. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 3(4), 361-371.
  38. Chen, J., Yan, L., Wang, S., & Zheng, W. (2024). Deep Reinforcement Learning-Based Automatic Test Case Generation for Hardware Verification. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 409-429.
  39. Wang, S., Chen, J., Yan, L., & Shui, Z. (2025). Automated Test Case Generation for Chip Verification Using Deep Reinforcement Learning. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 4(1), 1-12.
  40. Fan, J., Trinh, T. K., & Zhang, H. (2024). Deep Learning-Based Transfer Pricing Anomaly Detection and Risk Alert System for Pharmaceutical Companies: A Data Security-Oriented Approach. Journal of Advanced Computing Systems, 4(2), 1-14.
  41. Bi, W., Trinh, T. K., & Fan, S. (2024). Machine Learning-Based Pattern Recognition for Anti-Money Laundering in Banking Systems. Journal of Advanced Computing Systems, 4(11), 30-41.
  42. Rao, G., Trinh, T. K., Chen, Y., Shu, M., & Zheng, S. (2024). Jump Prediction in Systemically Important Financial Institutions' CDS Prices. Spectrum of Research, 4(2).
  43. Zhang, H., Feng, E., & Lian, H. (2024). A Privacy-Preserving Federated Learning Framework for Healthcare Big Data Analytics in Multi-Cloud Environments. Spectrum of Research, 4(1).
  44. Chen, C., Zhang, Z., & Lian, H. (2025). A Low-Complexity Joint Angle Estimation Algorithm for Weather Radar Echo Signals Based on Modified ESPRIT. Journal of Industrial Engineering and Applied Science, 3(2), 33-43.