AI-Powered Based Fraud Detection Using Graph Neural Network for Mobile Payment System
DOI:
https://doi.org/10.32628/CSEIT2511677Keywords:
AI, Graph Neural Networks (GNN), SMOTE-GAN, Meta-Learning, Heterogeneous Graph Neural Networks (HGNN), Edge-Level Real-Time Risk Scoring, Mobile Payment Fraud, Synthetic Data Generation, Risk Prediction, Adaptive Optimization, Classification Accuracy, Risk AssessmentAbstract
Artificial Intelligence (AI) enables machines to perform tasks that require human-like intelligence, including the prediction and detection of anomalies.The GRAPH Neural Networks (GNNs) can be successfully used in mobile payment systems to model user-user, device-user, merchant-user, and transaction relationships and detect fraud based on the pattern of relationships. Nevertheless, traditional approaches to AI face the challenges of massive imbalanced data, changing feature usefulness, multi-entity interactions, and real-time detection. To address these obstacles, we combine the best methods in our workflow. The proposed method, Edge-Level Real-Time Graph Neural Network Risk Scoring (ELRLVN2RS), allocates real-time risk ratings on transactions during the streaming mode, which allows the industry to provide high-risk activity alerts immediately. Additionally, synthetic fraud examples produced by SMOTE-GAN seem realistic and balance the PaySim data set, and they improve the acquisition of rare patterns of fraud. Furthermore, Meta-Learning of Feature Selection is a dynamic feature selection model that selects the most predictive features, adapting to the evolving fraud patterns as well as removing noise. Finally, Heterogeneous Graph Neural Networks (HGNNs) learn both local and global patterns of many entities, and this is because they capture the intricate interactions between them. The presented method AI-GNN framework that offers mobile payment fraud detection based on adaptive, heterogeneous, and real-time risks.
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