Deep Neural Network–Enhanced Credit Card Fraud Detection in Real-Time Payment Systems: Integrating IAM, Cloud Security, and AI-Powered Chatbot Intelligence
DOI:
https://doi.org/10.15662/IJRAI.2023.0604003Keywords:
Deep Neural Networks (DNNs), Real-Time Payment Security, Credit Card Fraud Detection, Identity and Access Management (IAM), Cloud-Native Security, AI-Powered Chatbots, Anomaly Detection, Zero-Trust Architecture, Secure Payment Analytics, Scalable Fraud IntelligenceAbstract
Real-time payment ecosystems demand highly resilient, intelligent, and adaptive fraud detection mechanisms to counter rapidly evolving cyber threats. This study presents a Deep Neural Network–Enhanced Fraud Detection Framework designed for large-scale credit card transactions processed across cloud-native payment infrastructures. The proposed system integrates Identity and Access Management (IAM), cloud security controls, and AI-powered chatbot intelligence to achieve continuous, automated risk monitoring. The DNN architecture incorporates feature-aware embedding layers and anomaly-sensitive prediction modules that dynamically learn behavioural deviations in transaction patterns, significantly improving detection accuracy and reducing false positives.
To strengthen end-to-end security, the framework aligns with zero-trust IAM principles, multi-factor authentication, role-based access control, and encryption-by-default policies. Cloud-native services—such as event-driven data pipelines, autoscaling clusters, and policy-enforced microservices—enable low-latency prediction and rapid operational response. Additionally, an AI-powered security chatbot provides real-time alerts, self-serve analytics, and automated incident triage, enhancing both user experience and SOC-team productivity.
Experimental evaluation on high-volume transaction datasets demonstrates superior precision, adaptability, and robustness compared to traditional ML models. The integration of deep learning, cloud security, and conversational AI establishes a unified, intelligent, and scalable approach for secure digital payment operations.
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