Regulatory Compliance

BCBS 239 Principles for Effective Risk Data Aggregation and Risk Reporting

Angelika Sebokova
August 19, 2021 | 3.5 min read
"One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks’ information technology (IT) and data architectures were inadequate to support the broad management of financial risks." – Basel Committee on Banking Supervision (BCBS).

The BCBS 239 compliancy self-assessment study conducted by 30 G-SIBs has demonstrated that banks are least compliant with respect to Principle 2: "Data Architecture and IT Infrastructure", within Section I. “Overarching Governance and Infrastructure". The primary reason indicated was the non-availability of data taxonomies and poor data ownership.

Since business definitions may not be aligned across business lines, this may lead to contradictions in resultant calculations, inconsistent reporting, misleading analytical results, and an increase in undertaking data correction and reconciliation. Business and IT units appear to speak different languages, resulting in increased efforts for IT analysis and implementation. Sound familiar?


  • Is your institution facing challenges due to business definitions and data requirements which are non-harmonized and throughout your organization?

  • Does your institution lack understanding and vision concerning which data is available, from where it is sourced, and transparency concerning who is responsible for providing the quality assurance for which data element?

  • Does your institution have communication issues between business and IT units concerning the provision and source of the data, leading to increased effort and slow time-to-market?

  • Does your business lack confidence about how to "start small, but think big"?


Your institution’s key focus should be the establishment of a taxonomy and glossary of risk data to maintain a comprehensive business information model-structured representation of data requirements, common language for business and IT units, business and technical metadata, across the group and between business lines.

Simplity’s fast, efficient, and flexible business data modelling (BDM) methodology provides a unified language across all areas of the group and between business lines. Simplity’s all-in-one data intelligence platform Accurity can provide harmonized naming conventions, resulting in physical data models which are easily maintainable, adaptable, and logical via the Business Glossary and Data Catalog solution. The resultant atomic level data will be consistent across the organization, thus enabling aggregation and flexible reporting, with drill-down possibilities. BDM is the first step in your institution’s roadmap towards a "single version of the truth" consolidated data source.

Your institution should also focus on the provision of data governance. Does your institution have a vast number of data silos in the current BI environment which are generating high maintenance and operational costs, leading to an inability to support and comply with business needs and regulatory requirements in a timely manner? The resultant effect is poor data quality leading to consequently higher risks!

By maintaining the references to data requirements and IT systems, your institution can ultimately improve data quality significantly, ensure traceability, and enable consistency. To comply with BCBS 239, data-quality management must also be established by your institution, including data profiling, data lineage, monitoring, reporting, and escalation procedures.

Our Accurity data intelligence platform offers a Data Quality and Data Observability solution that facilitates the following procedures: profiling, monitoring, controlling, and reporting on data quality and related events through an integrated technology platform. You can even try this data quality solution for free.

Don’t let poor information quality cost your institution. Simplity’s consultancy services and our Accurity platform provide support for the BCBS 239 Regulatory Principles, primarily for data governance, data architecture, accuracy and integrity, completeness, and adaptability.

Angelika Sebokova
Marketing Specialist