Cloud-Based Artificial Intelligence for Scalable Financial Analytics and Decision Support

Abstract

Financial institutions generate and consume data at a scale that on-premises infrastructure struggles to support. Transaction streams, market feeds, customer interactions, and regulatory reporting all demand elastic computing power, and artificial intelligence (AI) models need large datasets and specialized hardware to train and run. Cloud computing offers on-demand, scalable resources that make AI-driven financial analytics practical at enterprise scale. This review synthesizes fifteen studies on how cloud-based AI supports scalable financial analytics and decision support. It covers three themes: (1) cloud architectures and deployment models suited to regulated financial workloads; (2) scalable data processing and analytics, including distributed frameworks and service-oriented architectures; and (3) AI-enabled decision support, including explainability, workflow integration, and security. The review finds that cloud platforms remove key scalability barriers to financial AI. Their value, however, depends on choosing the right architecture, managing data governance and security, integrating analytics into decision workflows, and controlling risks such as concentration on a few cloud providers. The review proposes a layered framework and outlines directions for future research.