A Risk-Aware Generative AI and LLM-Driven Cloud Framework for Secure Banking and Trade Analytics in 5G Web Applications

Abstract

The rapid adoption of 5G-enabled web applications in banking and trade has accelerated transaction speeds, data flows, and digital interactions, simultaneously increasing exposure to cyber risks and financial fraud. Traditional security systems struggle to process real-time, high-velocity data streams with adaptive threats. This paper proposes a risk-aware, generative AI and Large Language Model (LLM)-driven cloud framework for secure banking and trade analytics in 5G web applications. The framework leverages cloud-native infrastructure for scalable, low-latency processing, while generative AI models simulate potential risk scenarios, anticipate threats, and provide decision-support insights. LLMs enhance interpretability, anomaly detection, and automated reporting. Integrated secure ETL pipelines ensure high-quality, consistent data from heterogeneous banking and trade sources. Risk-awareness modules quantify potential threats, prioritize interventions, and dynamically adjust system parameters to mitigate financial and cybersecurity risks. Evaluation on simulated and real-world datasets demonstrates improved anomaly detection accuracy, reduced false positives, and enhanced operational resilience. This study presents a blueprint for deploying intelligent, adaptive, and secure frameworks that combine generative AI, LLMs, cloud computing, and 5G web technologies for modern banking and trade analytics.