An End-to-End AI- and LLM-Enabled Cloud Ecosystem for Cybersecure Financial Fraud Detection and ETL-Driven Web Applications
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Abstract
Cybersecurity and fraud detection are among the most critical challenges facing modern financial ecosystems. This paper proposes an end-to-end cloud-native architecture that integrates Artificial Intelligence (AI) and Large Language Models (LLMs) with robust Extract, Transform, Load (ETL) workflows to support fraud detection and secure web application development. The system leverages scalable cloud infrastructure to ingest, process, and analyze large volumes of transactional data, applying LLM-derived semantic analysis and deep learning models to detect anomalous activity in real time. A combination of supervised and unsupervised learning techniques enhances the detection of both known and novel fraudulent patterns. Additionally, the platform enforces multi-layered cybersecurity protocols — including encryption, access control, and continuous threat monitoring — to defend against evolving attack vectors. Empirical evaluation on benchmark datasets demonstrates significant improvements in detection accuracy and reduction of false positives compared to traditional rule-based systems. The proposed architecture also modularizes ETL processes to streamline data integration and support dynamic web applications in finance. This architecture serves as a blueprint for organizations seeking scalable, intelligent, and secure financial data platforms.
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