Autonomous Data Warehousing for Financial Institutions: Architectures for Continuous Integration, Scalability, and Regulatory Compliance

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@article{1709984,
      author = {Olatunde Gaffar, Ayoola Olamilekan Sikiru, Mary Otunba, Adedoyin Adeola Adenuga},
      title = {Autonomous Data Warehousing for Financial Institutions: Architectures for Continuous Integration, Scalability, and Regulatory Compliance},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {2},
      pages = {332-347},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709984.pdf},
      abstract = {Autonomous Data Warehousing (ADW) represents a transformative shift in how financial institutions manage, scale, and secure their data infrastructure. As regulatory requirements become increasingly complex and real-time data analytics becomes critical to competitiveness, traditional data warehouses are proving inadequate in terms of scalability, integration speed, and compliance enforcement. This paper explores the architectural foundations and strategic value of ADW within financial institutions, with a particular focus on continuous integration (CI), scalability, and regulatory compliance. ADW architectures leverage machine learning, self-tuning capabilities, and cloud-native design to automate data ingestion, indexing, query optimization, and security enforcement. Through integrated CI pipelines, these systems enable seamless updates and schema evolution without disrupting operations?essential for supporting agile analytics, digital product development, and cross-functional reporting. Scalability is achieved through elastic compute-storage decoupling, distributed query execution, and resource auto-provisioning, ensuring institutions can handle massive volumes of transactional and unstructured financial data in real-time. Moreover, regulatory compliance?particularly with global standards such as Basel III, GDPR, and anti-money laundering (AML) frameworks?is embedded into the architectural fabric through policy-driven access controls, immutable audit logs, and encryption at rest and in transit. ADW platforms support metadata tagging, data lineage tracking, and automated anomaly detection, all of which enhance audit readiness and reduce compliance risk. The paper argues that the convergence of automation, cloud elasticity, and regulatory intelligence within ADW offers financial institutions a resilient and future-proof data backbone. As financial ecosystems become more decentralized and data-intensive, the adoption of autonomous data warehousing will be critical for institutions seeking to accelerate decision-making, reduce operational risk, and maintain regulatory trust. This architecture not only redefines technical possibilities but also sets a strategic precedent for the future of data-driven finance.},
      keywords = {Autonomous data, Warehousing, Financial institutions, Architectures,  Continuous integration, Scalability, Regulatory compliance},
      month = {August},
  }