Explainable Fraud Intelligence Using Graph Neural Networks and Real-Time Behavioral Analytics for Secure Digital Banking
Abstract
Digital banking has transformed financial services by enabling rapid, convenient, and highly scalable transactions, but it has simultaneously increased exposure to sophisticated fraud, account takeover, identity misuse, payment manipulation, and coordinated financial attacks. Conventional fraud detection systems primarily depend on transaction-level rules and isolated machine learning models, which may struggle to identify complex relationships among customers, accounts, devices, merchants, locations, and transactions. This research proposes an explainable fraud intelligence framework that integrates Graph Neural Networks (GNNs) with real-time behavioral analytics to identify suspicious activities while providing interpretable evidence for security decisions. The proposed approach represents banking interactions as dynamic graphs and applies graph-based learning to capture relational patterns, while behavioral analytics continuously evaluates deviations in transaction frequency, spending behavior, device usage, geographic activity, and authentication patterns. An explainability layer identifies influential relationships, transactions, and behavioral features contributing to fraud predictions. The methodology incorporates data preprocessing, dynamic graph construction, temporal feature engineering, GNN-based classification, real-time event processing, risk scoring, and explanation generation. The framework is designed to improve fraud detection accuracy, reduce false positives, support rapid investigation, and strengthen regulatory transparency. By combining relational intelligence with continuous behavioral analysis and interpretable machine learning, the proposed architecture provides a practical foundation for secure, adaptive, and trustworthy digital banking fraud prevention.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17335-17341 |
Published |
September 18, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Idris Abijo (2025). Explainable Fraud Intelligence Using Graph Neural Networks and Real-Time Behavioral Analytics for Secure Digital Banking. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 5 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17335-17341. https://doi.org/10.15662/IJAESIT.2025.0805017 |
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