A Systematic Framework for Data Lake Curation and Regulatory Com- pliance in Financial Institutions: Architecture, Implementation, and Best Practices

Abstract

This research develops a structured framework for Data Lake Curation tailored to regulatory compliance in financial institutions. The proposed framework encompasses several architectural layers: Data Ingestion, Data Storage, Data Processing and Transformation, Metadata Management, and Data Security and Access Control. The layers incorporate components and tools to facilitate accurate data collection, secure storage, quality processing, comprehensive metadata management, and robust security measures. Implementation involves the integration of heterogeneous data sources, continuous data quality management, and real-time compliance monitoring. The operational framework includes governance and policy management, auditing and reporting, and data retention and lifecycle management, ensuring alignment with regulatory requirements. Key challenges such as data retention, lineage, and access control are addressed through automated lifecycle management, integrated lineage tracking tools, and granular access control mechanisms. These best practices enable financial institutions to maintain compliance with regulations such as GDPR, Basel III, and Dodd-Frank while efficiently managing complex data environments.