Authors:
Ajay Tanikonda,Sudhakar Reddy Peddinti,Subba Rao Katragadda,DOI NO:
https://doi.org/10.26782/jmcms.2025.09.00011Keywords:
Artificial Intelligence (AI),Machine Learning (ML),Transformer Networks,Graph Neural Networks (GNNs),Fraud Detection,E-Commerce Security,Abstract
The growing prevalence of e-commerce in global digital economies attracts more advanced forms of fraudulent practices. Security methods from the past have shown their limitations against the combination of assaults that target identity checks, transaction authentication mechanisms, and data integrity systems. A detailed technical model of secure e-commerce system development emerges by integrating present-day technologies across AI/ML with Blockchain cryptography and Biometric signal processing systems. The discussion analyzes leading-edge AI structures, updated cryptographic algorithms, and integrated biometric methods, resulting in a single fraud detection platform. The project covers system integration difficulties while validating performance and delivering complete specifications at the mathematical, procedural, and protocol levels. The paper evaluates results against industry standards before examining how edge devices and federated learning models can implement this system.Refference:
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