GRA · Financial Regulation Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Supervisory data
Supervisors see firms mainly through the data firms report to them, and they act on what that data allows them to see. Supervisory data covers regulatory returns, granular loan and transaction records, and the internal risk data that firms aggregate for their own boards. This page explains how data gaps and quality failures hide risk, how reporting standards and identifiers make data comparable, how supervisory technology is changing the work, and who decides what is collected.
How the work runs.
Supervisory review console
Supervisory data · Bank supervision · Cycle 2
Firms by risk rating
| Bank B | High | 3 | 4 | Enhanced |
| Bank D | Medium | 2 | 2 | Standard |
| Insurer Alpha | High | 3 | 4 | Enhanced |
| Fintech F1 | Low | 2 | 0 | Light |
Findings
Climate expectations met
share of firms, thematic review
Fig.Supervisory review console: example names and illustrative values, not a record or a live system.
Supervisory data: how the risk develops.
Risk data, quality and reporting. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Fragmented definitions
The same concept, such as default, exposure or counterparty, is defined differently across templates, regimes and firms. Aggregating across them without reconciliation produces totals that are precise and wrong.
Aggregation fails inside firms
Firms that cannot aggregate risk across legal entities and business lines quickly cannot see their own concentrations. In stress, supervisors asking for an urgent view find the data arrive late, incomplete or inconsistent.
Reporting lag hides fast risks
Quarterly returns describe a past position. Liquidity runs, margin calls and market moves unfold in days, so authorities increasingly ask for more frequent and more granular data in stress.
Burden crowds out usefulness
Templates added over decades overlap and duplicate, raising cost without adding insight. Integrated reporting, common data dictionaries and granular collection aim to report once and use many times.
What supervisory data means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Disclosure crosswalk
Where evidence on supervisory data fits the disclosure and prudential frameworks institutions report against.
| Pillar | TCFD | ISSB IFRS S2 | Prudential (BCBS, IAIS) | Evidence this page can hold |
|---|---|---|---|---|
| Governance | Board oversight of climate-related risks | Governance requirements | Governance and risk appetite principles | Who owns the risk and how it is escalated |
| Strategy | Risks, opportunities and resilience | Scenario analysis and resilience | Strategy and business model principles | Scenario design and transmission paths |
| Risk management | Identify, assess, manage | Risk management processes | Internal control and ICAAP or ORSA | Exposure maps and loss views |
| Metrics and targets | Metrics used and targets set | Cross-industry and industry metrics | Data, metrics and reporting principles | Indicator definitions and units |
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Return resubmission rate | percent of returns resubmitted after validation | quarterly | Supervisory reporting systems | Published statistics |
| Validation rule failures | count of failed checks per return | per submission | Taxonomy validation rules | Published statistics |
| Risk data aggregation principles adoption | assessment grade per principle for systemic banks | periodic | BCBS progress reports | Published statistics |
| Legal entity identifier coverage | percent of reported counterparties with an LEI | quarterly | Granular reporting and GLEIF data | OBSERVED |
| Reporting timeliness | days from reference date to submission | per return | Supervisory reporting calendars | Published statistics |
| Granular credit data coverage | percent of loans reported at loan level | annual | Credit register documentation | Published statistics |
| Supervisory technology use | presence and type of tools by authority | per survey round | Authority surveys | REPORTED |
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| EBA reporting frameworks and data point model | Templates, definitions and validation rules for EU supervisory reporting | Public, reuse with attribution |
| ECB AnaCredit documentation and statistics | Loan-level credit data definitions and aggregate statistics for the euro area | Public, ECB reuse policy with attribution |
| GLEIF Global LEI Index | Legal entity identifiers with reference and ownership data | CC0, public domain |
| FFIEC Central Data Repository | Call reports of US insured banks with definitions | Public domain, US government work |
| BCBS progress reports on risk data aggregation and risk reporting | Assessments of systemic banks against the principles | Public, BIS terms of use with attribution |
| BIS Financial Stability Institute publications on suptech | Surveys and case studies of supervisory technology | Public, BIS terms of use with attribution |
| IMF Financial Soundness Indicators | Harmonised indicators of deposit takers and other sectors with compilation metadata | Public, IMF terms |
Who decides, and what the evidence serves.
Financial Regulation Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Prudential supervisors and central banks | What to collect, at what granularity and frequency, and how to validate it | Definition maps, burden and duplication evidence, quality practice |
| Statistical authorities and standard setters | Whether to integrate statistical and supervisory reporting into common models | Comparative reporting frameworks and identifier coverage |
| Legislatures | What legal gateways allow authorities to share confidential data | Comparative confidentiality and data-sharing provisions |
| Boards of financial firms | Whether risk data can be aggregated accurately, completely and quickly in stress | Supervisory expectations and assessment practice for risk data aggregation |
Outputs this topic produces
- Data dictionaries mapping definitions across reporting regimes
- Briefs on risk data aggregation practice and its supervision
- Learning material on supervisory technology with its stated limits
- Quality checklists for using public supervisory data
What experts ask.
Researchers on supervisory data ask whether the data can show the risks that matter.
- Does supervisory data capture the exposures and interconnections that drive systemic risk?
- Why do data quality and risk data aggregation problems persist in large institutions?
- How can reporting burdens be reduced while improving data quality?
- How do common data standards and identifiers improve comparability?
- How are new data sources and analytical tools changing supervision?
Data that arrives late, differs and misses things.
Supervisory reporting has grown through layers of templates added after successive crises, often with inconsistent definitions. Large institutions struggle to aggregate risk data across legal entities and systems, a weakness exposed during past crises when firms could not quickly report their exposures. Data that is late, inconsistent or incomplete limits supervisors' ability to detect emerging risks.
Reforms aim to standardise definitions, use common identifiers for entities and instruments, and move towards granular data from which authorities can derive what they need. Supervisory technology applies analytics and machine learning to this data. Each step raises questions of governance, confidentiality and the cost of change.
How the evidence is built, and where it stops.
- 01
Data standards analysis
Examining definitions, identifiers and formats with data engineering, interoperability and stewardship. Standards adoption is uneven.
- 02
Comparative reporting review
Comparing reporting regimes across jurisdictions. Detailed returns are confidential.
- 03
Case studies
Learning from data failures in past episodes. Public detail is limited.
- 04
Published data analysis
Using public aggregates. They may hide variation.
How it is studied, and what the methods cannot say.
- 01Data engineering, interoperability and stewardshipMapping templates to a common model · Entity resolution with identifiersApplied here
- 02Policy, institutional and decision scienceRegulatory impact assessment · Comparing supervisory architecturesApplied here
- 03Financial economics and macro-financeCosts and benefits of capital requirements · Tracing leakage of macroprudential measuresApplied here
Questions and answers.
What is a legal entity identifier?
Why does granular data matter?
What is suptech?
Why is risk data aggregation hard?
Does the platform access supervisory data?
Will common data standards reduce reporting burden?
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