Enterprise-Wide Data Governance and AI Ethics Transformation for a Global Investment Bank
A reference governance operating model to meet regulatory mandates, strengthen data integrity, and enable responsible AI at scale.
Executive Summary
This case study presents a reference enterprise-wide data governance and AI ethics framework informed by practitioner-led delivery experience associated with SentinelX Digital. The framework illustrates how global investment banks can align data governance operating models with regulatory mandates such as BCBS 239 and GDPR while preparing data foundations for responsible AI adoption.
The reference model demonstrates how standardized data definitions, enhanced lineage visibility, and integrated data quality and compliance controls can be applied across multi-jurisdictional banking environments.
Challenge
Large investment banks often operate fragmented data ecosystems spanning multiple business units, legal entities, and geographies. Inconsistent metadata standards, manual compliance checks, and siloed governance practices increase the risk of regulatory non-adherence and reporting delays.
In the absence of a unified governance operating model, organizations also face limitations in scaling advanced analytics, AI initiatives, and self-service data consumption — particularly under heightened regulatory scrutiny.
Comparable banking institutions required a single, enterprise-wide governance framework capable of supporting regulatory compliance, data integrity, and future AI readiness simultaneously.
Reference Data Governance & AI Ethics Framework
This case study outlines a multi-layered data governance operating model informed by delivery patterns observed across large BFSI governance programs.
The framework integrates policy, process, and technology alignment to support consistent governance execution at scale. Typical components include:
- Centralized metadata and lineage repositories supporting stewardship and accountability
- Defined data ownership models across risk, finance, and compliance functions
- Automated data quality monitoring and remediation workflows
- Regulatory mappings (e.g. BCBS 239, GDPR) embedded into enterprise data catalogs to support audit readiness
Comparable programs have applied hybrid delivery models to enable phased adoption across business lines while maintaining consistent global compliance coverage.
Outcomes & Impact
Comparable enterprise banking programs applying this framework have demonstrated:
- ~99% regulatory compliance readiness for BCBS 239 and GDPR within defined transformation timelines
- ~40% reduction in manual data quality effort through automation and stewardship workflows
- Improved lineage visibility across thousands of critical data elements
- A scalable governance operating model supporting AI readiness and self-service analytics
Disclaimer
This anonymized case study illustrates reference governance methodologies and operating models informed by practitioner-led engagements associated with SentinelX Digital.
All metrics are indicative of outcomes typically observed in comparable BFSI governance and compliance programs.
Client identities, delivery responsibilities, and implementation specifics have been anonymized and generalized to preserve confidentiality.