Scalable AI Infrastructure for Large-Scale Financial Data Processing Using Cloud Computing

Abstract

Financial institutions process data at an extraordinary scale and speed. Market data arrives in millions of messages per second, payments must be scored for fraud in milliseconds, and regulatory stress tests require vast computations within fixed deadlines. Artificial intelligence (AI) adds further demands: training models on years of high-frequency data, serving predictions with strict latency, and processing unstructured documents. On-premises infrastructure sized for peak load is costly and slow to change. Cloud computing offers elasticity, but using it well for regulated financial AI requires deliberate architecture. This article proposes a reference infrastructure for scalable AI-driven financial data processing. It first characterizes six financial workload types by latency, volume, and computing profile. It then sets out a layered cloud infrastructure: workload placement across deployment models, container orchestration, lakehouse storage, stream processing, distributed training, and model serving. Operations are handled by bounded autonomous agents working through configurable change workflows, with resilience requirements drawn from financial regulation. Using a design-oriented approach grounded in ten studies published between 2016 and 2025, the article illustrates the infrastructure with a bank running real-time fraud scoring, daily market risk, and a quarterly stress test. It argues that scalable financial AI depends on matching infrastructure patterns to workload characteristics and on operating that infrastructure autonomously within governed limits.