A CLOUD-BASED AI FRAMEWORK FOR REAL-TIME FINANCIAL DATA VISUALIZATION AND DECISION SUPPORT

Abstract

The dynamic and volatile nature of global financial markets necessitates intelligent, scalable, and low-latency analytical infrastructures capable of supporting real-time decision-making. Traditional financial forecasting systems struggle to process high-frequency data streams, adapt to rapid market transitions, and deliver actionable insights at scale. To address these challenges, this paper presents a cloud-native artificial intelligence framework that integrates real-time data ingestion, deep learning-based prediction models, and interactive visualization dashboards for automated financial decision support. The architecture leverages event-driven streaming pipelines, microservices orchestration, and distributed storage to process market feeds and sentiment data in real time. Advanced forecasting models including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer networks are implemented and benchmarked. Experimental results demonstrate that the Transformer model achieves the highest directional accuracy, while GRU yields the lowest inference latency, establishing a clear trade-off between predictive precision and execution speed. The framework’s ability to auto-scale, monitor streaming workloads, and generate live investment recommendations validates its applicability for algorithmic trading, fintech advisory systems, and institutional market intelligence solutions. This research contributes an end-to-end, production-oriented blueprint for deploying AI-driven decision support within cloud-based financial environments.