Abstract

The rapid growth of cloud-based web applications and digital financial services has significantly increased the complexity of security threats and financial fraud. Traditional rule-based security systems and isolated analytics platforms are no longer sufficient to address sophisticated cyberattacks and evolving fraud patterns. This paper proposes a cloud and network integrated architecture leveraging artificial intelligence (AI) and large language models (LLMs) to enhance the security of web applications and enable advanced financial fraud analytics. The architecture combines intelligent extract–transform–load (ETL) pipelines, network-aware monitoring, AI-driven anomaly detection, and LLM-based reasoning to deliver real-time and scalable analytics. By integrating cloud infrastructure with network telemetry and financial transaction data, the proposed solution enables holistic visibility, adaptive threat detection, and explainable fraud insights. Experimental evaluation and use-case analysis demonstrate improved detection accuracy, reduced response time, and enhanced system resilience compared to traditional security and fraud detection approaches.