Integrating Blockchain-Assisted Transformer-CNN Architectures with Optimal Feature Selection for Robust Real-Time Digital Payment Fraud Mitigation

• Sarah Kim

  Department of Computational Intelligence and Cyber-Security, University of Toronto, Canada

  Author

The rapid proliferation of digital payment systems has revolutionized the global economic landscape, yet it has simultaneously exposed financial infrastructures to sophisticated fraudulent vectors that bypass traditional rule-based security protocols. This research presents an extensive theoretical and empirical exploration into a novel Blockchain-Assisted Transformer-CNN framework designed for real-time anomaly detection and fraud prevention. By leveraging the immutable ledger capabilities of blockchain technology alongside the predictive power of hybrid deep learning architectures-specifically Convolutional Neural Networks (CNNs) for spatial feature extraction and Transformers for temporal sequence modeling-this study addresses the critical limitations of legacy systems. A central focus is placed on Optimal Feature Selection (OFS) to mitigate the "curse of dimensionality" inherent in high-frequency transactional data, ensuring low-latency execution on resource-constrained edge devices. The methodology elaborates on the convergence of decentralized identity management, multi-factor authentication, and Variational Autoencoders for advanced outlier detection. Theoretical analysis reveals that the integration of blockchain not only ensures data integrity but also provides a decentralized trust network for customizable security challenges. The article concludes that the synergistic application of deep spatial-temporal modeling and distributed ledger technology offers a superior defense mechanism against contemporary cyber threats, including data breaches and malicious attacks that threaten corporate growth. This comprehensive review and framework serve as a foundational pillar for future research in secure, autonomous financial ecosystems.

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