GRA · Financial Regulation Nexus
Spatial financeWhere capital meets hazard, exposure and transition
Financial data disclosure
Markets, depositors and supervisors rely on what financial firms disclose: financial statements, prudential disclosures under Pillar 3, solvency reports for insurers and, increasingly, sustainability information. Disclosure works only when the data are reliable, comparable and usable. This page explains how disclosure quality degrades, how accounting and prudential measures diverge, how structured reporting standards help, and who sets and enforces the rules.
Financial data disclosure on the record.
Financial data disclosure: how the risk develops.
Prudential source quality, disclosures and reporting standards. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Accounting and prudential numbers diverge
Expected credit loss under IFRS 9, regulatory expected loss and capital filters measure related things differently. Users who compare across firms or frameworks without reconciliation draw false conclusions.
Discretion shapes presentation
Firms choose emphasis, segments and alternative performance measures. Selective presentation can comply with rules while obscuring risk, so enforcers review consistency as well as compliance.
Unstructured data blocks comparison
Disclosures published as documents rather than tagged data must be transcribed before analysis, introducing errors and delay. Structured formats such as inline XBRL make disclosures machine-readable.
Assurance has gaps
Financial statements are audited, but prudential disclosures and sustainability information receive uneven or limited assurance. Errors in unassured data can persist until a supervisor or market participant finds them.
Transition and physical risk on a portfolio.
Three scenario families after the NGFS framework move a sector portfolio through carbon costs and warming damages; the transmission view follows the losses to lenders, investors, public finances and households. Scenario: named assumptions about the future, not a forecast.
Controls
- Carbon price at the horizon, orderly path
- 200 per tCO2
- Delay before a disorderly transition
- 50% of horizon
- Cost passed to customers
- 50%
- Warming today above pre-industrial
- 1.30°C
- Warming per decade, current policies
- 0.25°C
- Damage coefficient
- 0.24% per °C²
- Years ahead
- 25 years
Readouts
- Worst scenario at the horizon
- −33.6% of value
Numbers, methods and sources
| Year | Orderly transition (% value) | Delayed, disorderly transition (% value) | Current policies (% value) |
|---|---|---|---|
| 0 | −0.38 | −0.38 | −0.38 |
| 3 | −2.87 | −0.53 | −0.67 |
| 6 | −5.36 | −0.68 | −0.97 |
| 9 | −7.84 | −0.84 | −1.27 |
| 12 | −10.33 | −0.99 | −1.57 |
| 15 | −12.82 | −7.12 | −1.87 |
| 18 | −15.31 | −15.05 | −2.17 |
| 21 | −17.80 | −23.00 | −2.48 |
| 24 | −20.29 | −30.94 | −2.79 |
Methods
- Climate scenario families NGFS (2023), Climate Scenarios for central banks and supervisors, Phase IV
- Climate stress test of the financial system Battiston and others (2017), Nature Climate Change 7
- Damage function Nordhaus (2017), Revisiting the social cost of carbon, PNAS 114
- Transmission channels BCBS (2021), Climate-related risk drivers and their transmission channels
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Urban extents Natural Earth
- Seaports Natural Earth
- Past year, daily temperature (ERA5 via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus ERA5 (C3S/ECMWF) · register open-meteo.public-pages
- Surface air temperature, daytime (AIRS) NASA AIRS · register nasa.gibs.context-live-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Financial Regulation Nexus in Foundry
- Tested at Financial Regulation Nexus in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Financial Regulation Nexus in Reports
- Taught in Faculty of Finance and Economics in Academy
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 |
|---|---|---|---|---|
| Pillar 3 disclosure timeliness | days from reference date to publication | per period | Bank disclosures | REPORTED |
| Filings in structured format | percent of issuers filing in inline XBRL | annual | Filing repositories | OBSERVED |
| Financial reporting enforcement actions | count by enforcer | annual | Securities regulator enforcement reports | Published statistics |
| Financial statement restatements | count per year | annual | Filing repositories | REPORTED |
| Sustainability standards adoption | adoption status per jurisdiction | annual | IFRS Foundation jurisdictional profiles | Published statistics |
| Audit inspection deficiencies | percent of inspected engagements with deficiencies | annual | Audit oversight inspection reports | Published statistics |
| Validation warnings in published disclosures | count per filing | per filing | Taxonomy validation of filings | OBSERVED |
What financial data disclosure 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 financial data disclosure 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 |
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 Pillar 3 data hub | Centralised prudential disclosures of EU banks in a common format | Public, reuse with attribution |
| SEC EDGAR | Filings of US registrants with structured financial data | Public domain, US government work |
| filings.xbrl.org | Index of European single electronic format filings | Open, terms per index |
| IFRS Foundation jurisdictional profiles | Adoption of IFRS accounting and sustainability standards by jurisdiction | Public, IFRS Foundation terms |
| ESMA reports on enforcement of financial information | Priorities and actions of European enforcers | Public, reuse with attribution |
| PCAOB inspection reports | Inspection findings for audit firms of US issuers | Public domain, US government work |
| FFIEC Central Data Repository | Call reports of US insured banks | Public domain, US government work |
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 |
|---|---|---|
| Accounting and sustainability standard setters | How standards define and require disclosure | Comparative adoption records and documented comparability problems |
| Securities regulators and enforcers | Which disclosures to review and when to act | Enforcement priorities and restatement and filing records |
| Prudential supervisors | How Pillar 3 and solvency disclosures are specified and checked | Disclosure formats, timeliness and quality evidence |
| Audit committees and audit oversight bodies | What assurance applies to which disclosures | Inspection reports and assurance standards |
Outputs this topic produces
- Reconciliation guides between accounting and prudential measures
- Comparative briefs on disclosure regimes and their enforcement
- Data guides to structured filings with quality checks stated
- Learning material on assurance of prudential and sustainability data
What experts ask.
Disclosure researchers ask whether published information is reliable enough to act on.
- How comparable are prudential disclosures across firms and jurisdictions?
- How do accounting and prudential measures differ, and why does it matter?
- What determines the quality and reliability of disclosed data?
- How are climate and sustainability disclosures developing, and how reliable are they?
- How can users assess the provenance and limits of disclosed information?
Disclosure works only if it is reliable.
Public disclosure supports market discipline: investors, creditors and counterparties can assess firms' risks and price them. It depends on consistent definitions, independent assurance and timely publication. Accounting standards and prudential frameworks measure different things for different purposes, and differences between them can confuse users.
Newer disclosure areas, such as climate and sustainability, rely on emerging standards, estimates and third-party data with significant uncertainty. Without clear provenance and methods, disclosures can create a false sense of precision. Research examines how disclosure regimes affect behaviour and how data quality can be improved.
How the evidence is built, and where it stops.
- 01
Disclosure comparison
Comparing disclosures across firms with financial economics. Definitions differ.
- 02
Data quality assessment
Assessing completeness and consistency with data engineering, interoperability and stewardship. Underlying data is not always available.
- 03
Standards analysis
Comparing reporting standards. Standards evolve.
- 04
Provenance tracking
Documenting sources and methods of disclosed data. Third-party data methods may be proprietary.
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
- 02Financial economics and macro-financeCosts and benefits of capital requirements · Tracing leakage of macroprudential measuresApplied here
- 03Policy, institutional and decision scienceRegulatory impact assessment · Comparing supervisory architecturesApplied here
Questions and answers.
What is Pillar 3?
Why does structured reporting matter?
Are Pillar 3 disclosures audited?
Why do prudential and accounting numbers differ?
Are climate disclosures reliable?
Does the platform verify firms' disclosures?
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