Title: GRAPH-ENHANCED TRANSFORMER NETWORK FOR FRAUD DETECTION IN DIGITAL BANKING: INTEGRATING GNN AND SELF-ATTENTION FOR END-TO-END TRANSACTION ANALYSIS |
Author: Ramya Lakshmi Bolla, Rajeswaran Ayyadurai, Karthikeyan Parthasarathy, Naresh Kumar Reddy Panga, Jyothi Bobba and R. Pushpakumar |
Abstract: Digital banking fraud detection is a dynamic issue because of the nature and sheer number of transactions. Conventional machine learning-based models tend to be challenged with high-dimensional input, real-time processing, and dynamic patterns in fraud. We address these drawbacks by introducing the Graph-Enhanced Transformer Network (GETNet), a mixed deep learning approach combining Graph Neural Networks (GNNs) and Transformer self-attention-based mechanisms for better fraud detection. GETNet identifies transaction relationships through GNNs and uses Transformers for sequential anomaly detection. Experimental results on the PaySim dataset show that GETNet is 99.5% accurate, far superior to traditional approaches like Decision Trees, Support Vector Machines, and Naïve Bayes. The model ensures scalability, flexibility, and real-time detection, which makes it a strong candidate for contemporary banking fraud detection. |
Keywords: Fraud Detection, Digital Banking, Graph Neural Networks, Transformers, Financial Security. |
DOI: https://doi.org/10.38193/IJRCMS.2025.7217 |
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Date of Publication: 26-03-2025 |
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Published Vol & Issue: Volume 7 Issue 2 March-April 2025 |