AI-Driven Cloud BI: Enhancing Predictive Analytics for Financial Insights

Abstract

Financial decision-making relies heavily on accurate predictive analytics, yet traditional business intelligence (BI) methods often struggle with scalability, computational efficiency, and insights. Conventional approaches cannot efficiently process large-scale financial datasets and optimize cloud resource utilization, leading to increased costs and slower performance. To address these limitations, The method proposes an AI-driven, cloud-based BI model that leverages lightweight AI for predictive analytics using the Global Financial Development Database (GFDD). The approach integrates cloud computing for seamless data retrieval and storage, ensuring enhanced processing speed and cost efficiency. Unlike traditional methods, The model optimizes predictive accuracy (R² = 0.89 vs. 0.82), reduces training time (90 vs. 200 sec), and improves processing throughput (1800 vs. 1500 records/sec). The comparative analysis demonstrates superior business KPI improvement (25% vs. 15%) and cloud cost efficiency (85% vs. 70%), highlighting its effectiveness in financial forecasting. By leveraging cloud infrastructure, The method enables financial insights, outperforming existing cloud BI frameworks in adaptability and resource optimization. This research contributes to the field by demonstrating the benefits of integrating AI-driven forecasting with cloud-based BI for enhanced financial decision-making. Future work will explore adaptive AI models for anomaly detection, further enhancing financial predictive analytics and cloud efficiency.