Explainable Graph Learning For Anomaly Detection in Distributed Financial Data Systems

  • Authors

    • Jospeh Kuruvilla Software Engineer, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-V9I3P102

    Published 07-10-2026

  • Explainable Artificial Intelligence, Graph Neural Networks, Financial Anomaly Detection, Distributed Data Systems, Financial Fraud Detection, Temporal Graphs, Graph Explainability, Transaction Monitoring, Model Interpretability, Financial Cybersecurity

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    The rapid growth of digital banking, electronic payments, and financial technology has increased the complexity of financial anomaly detection. Traditional rule-based and machine learning methods often struggle to identify sophisticated fraud involving multiple accounts, connected entities, and time-dependent transactions. Graph neural networks (GNNs) can capture these relationships but face interpretability challenges in regulated financial environments. This paper proposes an explainable graph learning framework for anomaly detection in distributed financial systems. The framework integrates heterogeneous temporal graph construction, GNN-based representation learning, anomaly scoring, explanation generation, and distributed model monitoring. It identifies transaction-level, account-level, and subgraph-level anomalies by analyzing suspicious transactions, coordinated account behavior, temporal patterns, and structural deviations. A research methodology is outlined to evaluate detection accuracy, explanation fidelity, computational efficiency, robustness, and latency against conventional and graph-based methods. The framework aims to support transparent, scalable, and accountable financial anomaly detection, with its effectiveness requiring validation through empirical studies on reliable financial datasets.

  • References

    [1] Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: A survey. Data Mining and Knowledge Discovery, 29, 626–688. https://doi.org/10.1007/s10618-014-0365-y

    [2] Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Database Performance Optimization Using Machine Learning-Driven Query Workload Modeling. International Journal of AI, BigData, Computational and Management Studies, 2(4), 107-116.

    [3] Visweswaran, K. (2026, March). Cross-Layer AI Optimisation Techniques for Thermal Management in Wearable Intelligent Devices. In 2026 International Conference on Machine Learning and Autonomous Systems (ICMLAS) (pp. 1557-1564). IEEE. https://doi.org/10.1109/ICMLAS67792.2026.11483953

    [4] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-auditing deep learning pipelines for automated compliance validation with explainability, traceability, and regulatory assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133-142.

    [5] Cheng, D., Zou, Y., Xiang, S., & Jiang, C. (2025). Graph neural networks for financial fraud detection: A review. Frontiers of Computer Science, 19(9), Article 199609. https://doi.org/10.1007/s11704-024-40474-y

    [6] Virendra Jangid. (2024). Cloud-Native Financial Data Platforms for Risk and Compliance Management. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4389 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8349

    [7] Hamilton, W. L., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30 (pp. 1024–1034). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea0-Abstract.html

    [8] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). Large Language Model–Driven Intelligent Observability Frameworks for Serverless Applications in Event-Driven Cloud Architectures. International Journal of Emerging Research in Engineering and Technology, 6(1), 100-110.

    [9] Vasantha Kumar Hosahalli Seenappa. (2024). NG-iRTS: A Python and Ansible-Based Infrastructure as Code Framework for Enterprise Network Automation. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4486–4492. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8533

    [10] Gantikota, S. (2025). Privacy-By-Design Engineering Under GDPR and CCPA: Practical Patterns for Cross-Border Data Handling In Cloud-Based Applications. International Journal of AI, BigData, Computational and Management Studies, 6(1), 227-231. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I1P123

    [11] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127-135.

    [12] Khoozani, Z. S., Seera, M., & Lim, C. P. (2026). Towards transparent financial AI: A systematic review of graph learning and explainable methods for credit risk and fraud detection. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11645-z

    [13] Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1609.02907

    [14] Sunkara, R. (2024). Improving Observability and Stability in Wayland-Based Compositors: Lifecycle Logging, Buffer Validation, and Crash Hardening in Production Display Stacks. American International Journal of Computer Science and Technology, 6(1), 60-64. https://doi.org/10.63282/3117-5481/AIJCST-V6I1P106

    [15] Nangi, P. R., & Obannagari, R. N. CK, & Settipi, S.(2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104-113.

    [16] Y. Syed, “Enterprise Observability and Software Delivery Performance: From Telemetry to Engineering Intelligence”, IJADSMC, vol. 5, no. 2, pp. 01–08, Sep. 2022, doi: 10.67228/30713498/IJADSMC-2022PIIKP2M

    [17] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (pp. 4765–4774). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html

    [18] Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, Article 122156. https://doi.org/10.1016/j.eswa.2023.122156

    [19] Syed, Y. (2025). Generative AI Integration and RAG-Based Enterprise Tooling: Architectures, Techniques, and Organizational Impact. International Journal of Scientific Research in Science, Engineering and Technology, 12(6), 600-606. DOI: https://doi.org/10.32628/IJSRSET2613255

    [20] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123-135.

    [21] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778

    [22] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). Quantum-Enhanced Optimization Models for Large-Scale Security Policy Evaluation in Distributed Cloud-Native Systems. American International Journal of Computer Science and Technology, 7(6), 46-56.

    [23] Casadei, A., Bhaduri, S., Malshe, R., Mullapudi, P., Ratan, R., Pole, A., & Rakshit, A. (2025). Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis. arXiv preprint arXiv:2512.14764.

    [24] Nangi, P. R., & Obannagari, C. K. R. N. (2024). A Multi-Layered Zero-Trust–Driven Cybersecurity Framework Integrating Deep Learning and Automated Compliance for Heterogeneous Enterprise Clouds. American International Journal of Computer Science and Technology, 6(4), 14-27.

    [25] Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1710.10903

    [26] Ying, R., Bourgeois, D., You, J., Zitnik, M., & Leskovec, J. (2019). GNNExplainer: Generating explanations for graph neural networks. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc. https://proceedings.neurips.cc/paper/2019/hash/d80b7040b773199015de6d3b4293c8ff-Abstract.html

    [27] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). AI-Driven Data Engineering Pipelines for High-Velocity Analytics Using Serverless Architectures and Event-Stream Intelligence. International Journal of AI, BigData, Computational and Management Studies, 6(3), 74-83.

    [28] Syed, Y. (2024). DevSecOps and Enterprise-Scale CI/CD Pipeline Engineering: Governing Delivery Velocity, Embedded Security, and Release Automation Across Polyglot Environments. International Journal of Scientific Research in Science, Engineering and Technology, 11(5), 475-484. DOI: https://doi.org/10.32628/IJSRSET2513156

    [29] Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Autoscaling for Kubernetes Using Multivariate Time-Series Forecasting. International Journal of Emerging Research in Engineering and Technology, 2(2), 73-83.

    [30] Fung, W., Guo, L., Hilliard, D., Casadei, A., Ratan, R., Bhaduri, S., ... & Rakshit, A. (2025). OpComm: A Reinforcement Learning Framework for Adaptive Buffer Control in Warehouse Volume Forecasting. arXiv preprint arXiv:2512.19738.

    [31] Nangi, P. R., & Obannagari, C. K. R. N. (2026). Federated ai-driven query optimization for distributed cloud databases. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 91-99.

    [32] S. Suganya, Dr. Suresh Kumar Murugesan, and Dr. K. Govind, "Correlates of School and Family Environment on WellBeing," Journal of Survey in Fisheries Sciences, vol. 10, no. 2(S), pp. 1714-1720, 2023.

    [33] Nangi, P. R., & Nala Obannagari, C. K. R. (2026). Quantum-Classical Hybrid Algorithms for Complex Query Planning: A Scalable Computational Framework for High-Dimensional Decision Optimization in Heterogeneous Data Ecosystems. International Journal of AI, BigData, Computational and Management Studies, 7(2), 328-336. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P143

    [34] Brahmandam, L. M. K. (2024). An Empirical Evaluation of the Medallion Architecture on Databricks and Apache Spark with Snowflake: Throughput, Latency, and Cost for Batch and Real-Time Ingestion Patterns. International Journal of AI, BigData, Computational and Management Studies, 5(3), 197-206. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P122

    [35] Syed, Y. (2023). Polyglot Persistence and Enterprise Data Migration: Architectures, Consistency, and Practice. American International Journal of Computer Science and Technology, 5(3), 61-66. https://doi.org/10.63282/3117-5481/AIJCST-V5I3P106

    [36] Sandra, K. (2024). THE REGULATED BANKING AI LAKEHOUSE. INDO-CONTINENTAL ACADEMIC PUBLISHERS.

    [37] Nangi, P. R., & Obannagari, C. K. R. N. (2026). LLM-Driven Database Administration and Natural Language Query Interfaces. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 410-418.

  • Downloads

Similar Articles

1-10 of 113

You may also start an advanced similarity search for this article.