Govern financial data and scale trusted AI

BCBS 239 requires banks to produce accurate, timely, and comprehensive risk data through strong governance and data infrastructure. Most banks struggle with compliance, as regulations demand complete data lineage at the attribute level and automated risk aggregation capabilities. Actian supports BCBS 239 compliance through automated data lineage that traces data from source to report, real-time data quality indicators, and interactive lineage graphs that enable rapid risk data aggregation during stress situations.

Metadata management creates a “living map” of an organization’s data ecosystem, documenting where data originates, how it transforms, who accesses it, and what rules apply at every stage. For financial institutions, comprehensive metadata management enables accurate risk reporting by:

• Tracking all data sources used in risk calculations.
• Supporting regulatory compliance by providing the transparency required for regulatory frameworks.
• Facilitating AI governance by documenting training data and model inputs.
• Enabling faster incident response by quickly identifying affected systems and data flows.
• Reducing manual effort in compliance reporting through automated documentation and audit trails.

Effective data governance enhances fraud detection by providing real-time data transparency and accessibility across all systems, enabling faster identification of anomalies and suspicious patterns. By breaking down data silos and implementing automated monitoring controls that validate data flows between systems, financial institutions can detect inconsistencies like missing or duplicated transactions, unusual payment patterns, and account changes that may indicate fraud or phishing attempts. Modern AI-powered fraud detection systems require high-quality, well-governed data to achieve over 90% accuracy in identifying fraudulent activities while reducing false positives that frustrate legitimate customers.

Data quality is the foundation of AI readiness in financial services. Poor data quality directly undermines AI models used for credit scoring, fraud detection, risk assessment, and customer personalization, potentially leading to biased decisions, regulatory violations, and financial losses. To be AI-ready, financial institutions must implement robust data governance frameworks that ensure accuracy, completeness, consistency, and timeliness across all data sources, establish comprehensive data lineage to trace AI training data back to authoritative sources, implement automated data quality monitoring and validation, and maintain proper documentation for AI explainability and regulatory requirements, particularly under frameworks like the EU AI Act that classifies many financial AI applications as “high-risk.”

A business glossary ensures that critical financial terms such as “credit risk,” “default rate,” and “liquidity ratio” are defined consistently across all departments, reducing misunderstandings that can lead to compliance failures and operational errors. Without consistent definitions, different teams interpret data differently, creating risk in regulatory reporting and decision-making. Actian’s automated Business Glossary enables financial institutions to create and share standardized definitions across the enterprise, improving cross-functional collaboration, accelerating regulatory reporting, and ensuring everyone speaks the same data language when communicating with regulators and auditors.