RAN-AI Architectures Supporting Personalized Customer Interaction and Virtual Assistance in Banking Services
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I6P106Keywords:
RAN-AI Architectures, System-Level Network Design, AI-Assisted Decision Making, QoS- and QoE-Aware Scheduling, Contextual Recommendation and Assistance, Privacy-Preserving Machine Learning, Intelligent Wireless Access Networks, Regulatory-Compliant Network Design, Financial Service Communication Systems, Scalability and Computational Overhead, Stability Convergence AnalyticsAbstract
The integration of Radio Access Network Artificial Intelligence (RAN-AI) into digital banking transforms wireless financial services by enabling low-latency, secure, and highly personalized customer interactions at the network edge. This paper proposes a multi-layer RAN-AI architecture comprising Edge AI Controllers, Contextual Recommendation Engines, QoS-aware Resource Orchestrators, Federated Learning Modules, and Regulatory Compliance Units. The framework combines federated learning, deep reinforcement learning, and QoE-aware scheduling to optimize latency, throughput, and personalization accuracy under regulatory constraints. System-level simulations across dynamic banking scenarios—such as real-time advisory, chatbot transactions, fraud alerts, and omnichannel services—demonstrate up to 28% higher personalization accuracy, 35% lower latency, 22% improved QoE, and 31% reduced computational overhead compared to centralized AI models. The proposed architecture ensures privacy-preserving learning, regulatory compliance, scalability, and efficient wireless resource allocation, positioning RAN-AI as a key enabler of secure, intelligent, and QoE-driven digital banking ecosystems.
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