Data-Driven Supervision and CRT’s Planned Chartis Research Collaboration

CRT announcement and research status

CRT’s original announcement described a planned research collaboration with Chartis Research on data-driven financial supervision. This page presents CRT’s perspective on the proposed themes: data quality, reporting automation and supervisory analytics.

A completed report, its methodology and final author credits have not been linked in this announcement. The discussion below should therefore be read as CRT’s company perspective. Findings from a published report can be attributed and linked when that source is available.

Data quality as the foundation

In CRT’s view, useful supervisory analytics start with consistent definitions, traceable inputs and reliable validation. Greater data granularity can help supervisors examine the composition of reported figures, while clear lineage helps them understand how an input became an indicator.

Reporting automation should reduce avoidable manual handling and make exceptions visible. Its value depends on the reporting scope, the quality of incoming data and the institution’s operating processes. Outcomes should be assessed within those conditions.

Analytics with supervisory oversight

AI and machine learning can support the review of structured returns and narrative material. CRT’s approach emphasises explainability, audit trails and human review of consequential findings. Model outputs need to remain connected to their evidence and to the supervisory judgement they support.

Capital and liquidity analysis also depend on documented assumptions, consistent data and clear scenario definitions. An integrated reporting and analytical environment can help institutions review those dependencies together.

CORA’s approach

CORA combines data collection and governance, supervisory workflows, business intelligence and AI-assisted analysis. CRT proposes these capabilities as topics for research and discussion about modern supervisory infrastructure.

This announcement does not establish an independent assessment of CORA’s performance or architecture. Institutions evaluating a deployment should consider the documented project scope, measured results and their own acceptance criteria. For a source-linked example of CRT’s earlier work, see the BSP–R²A API prudential reporting case study.

Further research

Any future update about the Chartis collaboration will distinguish published findings from CRT’s commentary and provide the relevant source, methodology and author credits. No publication date is announced here.

About CRT

CRT’s current footprint spans 11 public institutions across 21 jurisdictions on 4 continents. The footprint includes supervisory authorities, contracting authorities and programme funders. Project scope and delivery stage vary by institution.