Deep Neural Network–Driven Credit Card Fraud Detection in Cloud Environments: Integrating Self-Serve Analytics, Cybersecurity Best Practices, and Quantum Machine Learning
DOI:
https://doi.org/10.15662/IJEETR.2023.0503003Keywords:
Deep Neural Networks (DNNs), Credit Card Fraud Detection, Cloud Computing, Self-Serve Analytics, Cybersecurity Best Practices, Quantum Machine Learning (QML), Anomaly Detection, Financial Threat Intelligence, AI-Powered Fraud Prevention, Scalable Cloud SecurityAbstract
The rapid expansion of digital payments and cloud-based financial services has increased the complexity and scale of credit card fraud, demanding more intelligent, adaptive, and secure detection mechanisms. This research presents a Deep Neural Network–driven credit card fraud detection framework optimized for cloud environments, designed to enhance scalability, latency performance, and real-time analytics. The proposed system integrates Self-Serve Analytics to empower security analysts and business users with on-demand insights, customizable dashboards, and automated anomaly exploration. To ensure robust protection across evolving threat surfaces, the framework incorporates cybersecurity best practices, including secure model deployment, continuous monitoring, encrypted data flows, and behavioral threat modeling. Additionally, Quantum Machine Learning (QML) components are introduced to evaluate quantum-enhanced classification strategies, demonstrating potential improvements in high-dimensional pattern recognition and anomaly detection. Experimental analysis using multivariate fraud datasets highlights superior performance in recall, precision, and detection latency compared to conventional cloud-based systems. The resulting architecture provides a scalable, secure, and intelligence-driven foundation for next-generation fraud detection in financial ecosystems.References
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