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.

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