International Journal of Science, Engineering and TechnologyAuthors: Madeline Turner, Eleanor Brooks, Thomas Ward, Chaitanya Srinivas, Aneesha Raj
Abstract: The rapid growth of digital banking, electronic payment systems, real-time transaction processing, and regulatory reporting has significantly increased the complexity and scale of financial information systems, creating a critical need for scalable and efficient data modeling frameworks. Traditional data models often struggle to support high transaction volumes, heterogeneous data sources, evolving business requirements, and stringent compliance standards while maintaining performance and data integrity. This paper presents a comprehensive framework for scalable data modeling in high-volume financial information systems that integrates enterprise data architecture, dimensional and relational modeling, metadata management, master data management, data governance, and cloud-native technologies into a unified architectural approach. The proposed framework incorporates modern data engineering practices, including distributed data processing, real-time data integration, event-driven architectures, artificial intelligence-assisted schema optimization, and automated data quality validation to improve scalability, consistency, and analytical performance. It further emphasizes governance-driven policies, regulatory compliance, role-based access control, data lineage, and security mechanisms to ensure the integrity, confidentiality, and traceability of financial data throughout its lifecycle. By leveraging cloud computing, intelligent automation, and high-performance database technologies, the framework enables organizations to efficiently manage large-scale transactional and analytical workloads while supporting business intelligence, fraud detection, risk management, customer analytics, and regulatory reporting. The proposed scalable data modeling framework provides financial institutions with a practical foundation for modernizing enterprise information systems, enhancing operational resilience, improving decision-making capabilities, and accelerating digital transformation in increasingly complex and data-intensive banking environments
DOI: http://doi.org/10.5281/zenodo.21848905
