Advanced International Journal of Multidisciplinary Research
E-ISSN: 2584-0487
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Volume 4 Issue 1
January-February 2026
| Author(s) | A K M Emran, Md Kamrul Islam, Md Ashraful Islam Nayem, Md. Tauhid Hossain Rubel, Syed Kamrul Hasan |
|---|---|
| Country | United States |
| Abstract | Abstract: Exploring GNNs as a cutting-edge approach to real-time detection of online money transfer fraud is the focus of this work. P2P payment systems, mobile money platforms, and decentralized financial infrastructures (DeFi) have all experienced explosive growth over the past decade due to their simplicity, speed, and affordability. Identity fraud, synthetic account misuse, coordinated fraud rings that exploit systemic vulnerabilities, and transaction laundering are some of the new types of fraud that can occur in these platforms, despite their desirability. In situations where fraud is predictable, isolated, and statistically distinct, logistic regression, rule-based algorithms, and standard ML models like Random Forests and SVMs have all proved effective in detecting it. Modern, hyper-connected, real-time financial ecosystems are seeing an uptick in non-linear, relational, and temporal fraud patterns, which these tactics struggle to combat. Because of their inherent bias, they fail to recognize the interconnected structural and relational processes that may point to coordinated fraud. The graph-like qualities of monetary exchanges, where elements (like IP addresses, users, and devices) are organically linked through edges that stand for transactions or relationships, are utilized by Graph Neural Networks to give a paradigm shift, on the other hand. Generalized neural networks (GNNs) are crucial for uncovering intricate fraud schemes because they represent these interactions as a graph structure that permits data to travel and accumulate across nodes. Because of this, the model may take global and regional effects into consideration. Relational learning excels when other methods fail, such as when trying to detect suspicious clusters of transactions, multi-hop collusions, or fraudulent subnetworks using separate features. In order to implement this method, we constructed an entirely new fraud detection system utilizing GNNs. Node feature engineering, graph generation, classification heads, message-passing layers, and a real-time processing optimized pipeline are all parts of it. We were able to empirically evaluate our technique using a real-world transactional dataset that was acquired from a leading financial services provider. As is typical in fraud detection tasks, the dataset had a highly skewed class distribution, which impacted both memory and accuracy. With an F1-score of 0.78, accuracy of 98.7 percent, precision of 0.81%, and recall of 0.76%, the model nevertheless performed admirably. The model's ability to detect fraudulent behaviors while maintaining dependable operations in the real world is demonstrated by these measures. Beyond its implications for technological performance, this study will help achieve broader aims in regulation, ethics, and national security. A number of federal agencies have issued advisories highlighting the need for strong, intelligent, and real-time fraud monitoring systems to safeguard national financial systems from fraudulent exploitation. These agencies include the DOJ, FinCEN, and DHS. Compliance with the USA PATRIOT Act and the Bank Secrecy Act (BSA) is of the utmost importance to financial institutions and fintech enterprises. As stated in the National Strategy to Combat Terrorist and Other Illicit Financing, they also want AI-driven surveillance systems to be resilient and explainable. This national goal is helped by our study, which provides a scalable, interpretable, and performance-driven GNN-based system. Along with helping with auditability, model explainability, and compliance reporting, all of which are crucial for regulated businesses, this strategy also helps with effective fraud detection. Integrating our suggested architecture for decentralized, privacy-preserving fraud detection into online learning extensions can further improve their functionality. Over time, these extensions can be integrated with federated learning systems and streaming data platforms. This work puts GNNs in a position to become a new weapon in the fight against digital payment fraud by combining cutting-edge graph representation learning with cybersecurity regulations and goals for financial integrity. Thanks to our research's careful analysis, innovative architecture, and adherence to statutory criteria, future financial systems will be reliable, safe, and robust. Additionally, it resolves a significant technical matter. |
| Keywords | Graph Neural Network, Fraud Detection, Digital Payments, Real-Time Systems, Deep Learning, Anomaly Detection, Financial Integrity, U.S. Cybersecurity |
| Discipline | Computer > Big Data / Data Science |
| Published In | Volume 3, Issue 5, September-October 2025 |
| Published On | 2025-09-06 |
| Cite This | Real-time Payment Fraud Detection Using Graph Neural Intelligence - A K M Emran, Md Kamrul Islam, Md Ashraful Islam Nayem, Md. Tauhid Hossain Rubel, Syed Kamrul Hasan - AIJMR Volume 3, Issue 5, September-October 2025. DOI 10.62127/aijmr.2025.v03i05.1137 |
| DOI | https://doi.org/10.62127/aijmr.2025.v03i05.1137 |
| Short DOI | https://doi.org/g98nd2 |
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