Modernizing Banking Compliance: An Analysis of AI-Powered Data Governance in a Hybrid Cloud Environment

Narendra Bhargav Boggarapu

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 2373-2381

Abstract: This article presents a comprehensive case study of a global investment bank's implementation of an artificial intelligence-powered data governance framework across its hybrid cloud infrastructure. The framework addresses the critical challenge of maintaining regulatory compliance while managing complex financial data across distributed environments. By leveraging advanced machine learning models for anomaly detection and automated metadata management, the bank established real-time monitoring capabilities that ensure compliance with major regulatory requirements, including GDPR and CCPA. The implementation successfully bridged the gap between on-premise and cloud environments, creating a unified governance approach that maintains data consistency and integrity. Key innovations include AI-driven alert systems for potential compliance violations, automated audit trail generation, and intelligent data classification mechanisms. The framework's success demonstrates the viability of AI-enhanced governance solutions in highly regulated financial environments, offering valuable insights for institutions facing similar challenges in the digital transformation era. This article details the technical architecture, implementation methodology, and outcomes, providing a blueprint for organizations seeking to modernize their data governance capabilities while maintaining strict regulatory compliance.

Keywords: Hybrid Cloud Governance; AI-Driven Compliance; Financial Data Management; Regulatory Technology (RegTech); Machine Learning Surveillance (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410612434
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Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:640

DOI: 10.32628/CSEIT2410612434

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