Cloud-Native AI Solutions for Data Quality and Integration in Finance

The increasing confluence of cloud computing and artificial intelligence (AI) is reshaping the financial services industry, with robust implications for data quality and integration. Financial institutions are encumbered by fragmented data architectures and low-quality datasets, which impede analytical accuracy, risk compliance, and real-time decision-making. This paper explores the design and deployment of cloud-native AI-powered pipelines engineered for cleansing, unifying, and enriching heterogeneous financial data in real time. This paper delves into the technical and organizational challenges endemic to legacy financial systems, survey state-of-the-art cloud-native AI architectural patterns addressing data quality, and present an integrated system framework employing microservices, data mesh, and event-driven streaming pipelines. The paper further details practical implementation approaches, metrics-driven evaluation strategies for assessing improvements in data quality and integration, technical considerations, and inherent limitations. Comprehensive discussion includes regulatory, security, and governance nuances, illustrated by recent case studies and emerging industry best practices. The synthesis charts a viable path forward for operationalizing scalable and compliant financial data ecosystems that are AI-ready for the requirements and risks of modern finance.