Synergistic Integration of Blockchain-Assisted Transformer-CNN Frameworks and Optimal Feature Selection for Enhanced Real-Time Digital Payment Fraud Mitigation

Abstract

The rapid digitalization of global financial systems has precipitated an unprecedented surge in digital payment volumes, concurrently providing a fertile landscape for increasingly sophisticated fraudulent activities. Traditional rule-based and shallow machine learning systems are increasingly inadequate against dynamic, multi-vector attacks. This research presents a comprehensive analysis and theoretical framework for a Blockchain-Assisted Transformer-Convolutional Neural Network (CNN) architecture designed for real-time fraud detection. By integrating the immutable, decentralized nature of blockchain with the advanced pattern recognition capabilities of hybrid deep learning models, this study addresses the critical vulnerabilities in modern banking infrastructure. A primary contribution of this framework is the application of optimal feature selection techniques, which reduce computational overhead and enhance detection precision by isolating the most predictive transactional variables. The methodology elaborates on the convergence of edge intelligence, intelligent contracts, and quantum-resilient strategies to safeguard financial ecosystems. Theoretical results suggest that the proposed synergistic approach significantly outperforms traditional models in terms of latency, resilience to adversarial tampering, and accuracy in high-volume environments. This article provides an extensive exploration of the cybersecurity landscape in digital banking across various regions, including Nigeria, Nepal, and the MENA region, offering a global perspective on risk management, consumer trust, and the future of decentralized financial security.