Cloud-Based Big-Data Architectures for AI-Driven Financial Planning and Analysis and Early Risk Signaling in Resource-Constrained Community Financial Institutions

Abstract

Cloud computing has moved from a peripheral IT decision to the default infrastructure assumption for financial institutions: 91% of financial-services firms globally have embarked on their cloud journey, up from just 37% in 2020, with North America leading at 98% initial adoption, and roughly 90% of financial-services respondents now operate hybrid or multi-cloud environments, against a core banking software market valued at $11.68 billion in 2023 and projected to reach $25.8 billion by 2032 [1][6]. Yet a 2024 cross-industry cloud-maturity survey found only 8% of organisations qualify as highly cloud-mature, indicating a substantial gap between adoption and effective use for most institutions, financial services included [1]. This article examines what cloud-based big-data architecture specifically, data lake, warehouse, and lakehouse patterns can offer resource constrained community banks, credit unions, and CDFIs building AI-driven financial planning and early risk-signaling capability, and what deployment model best fits their specific constraints. The technical case for cloud-based big-data architecture rests on a specific problem: financial institutions generate transaction, core-banking, and increasingly unstructured data (documents, communications, behavioural signals) far too voluminous and too varied in structure for traditional relational databases to handle cost-effectively, yet the risk-signaling and financial-planning use cases addressed elsewhere in this article series depend on exactly this combination of structured and unstructured data [4][5][9]. This article sets out a four-layer architecture ingestion, raw storage, curated storage, and serving evaluates three deployment models against the resource constraints typical of community financial institutions, and addresses the regulatory expectations the FFIEC's Cloud Computing guidance and related examiner practice now apply to cloud-hosted financial workloads.