XBRL Reporting for ESG Pillar 3 Disclosures
What is XBRL and why is it important?
Requirements for XBRL and Extensible Markup Language (XML) filings are becoming more commonplace for financial institutions – initially encompassing financial and capital reporting (FINREP and COREP) and shareholding disclosure for various jurisdictions, and more recently, with the EBA’s ESG Pillar 3 disclosure. Machine-readable disclosure requirements result in standardized regulatory filings, reduced risk of human error in data entry, and improved data analysis, all facilitating easy comparison with external data and perfect alignment with the European Commission’s (EC) financial-sustainability goals.
However, financial institutions will not be the only ones impacted by the move to machine-readable formatting in this sustainability drive. In fact, the EC is exploring XML disclosures for the EU Emissions Trading System, a cap-and-trade system aimed at reducing greenhouse gas (GHG) emissions. The drive would also impact non-financial corporations; but unlike their financial counterparts who already have a handle on XBRL/XML reporting as part of other regulations, corporations are ill prepared to manage the related upcoming challenges.
It also appears that the ESG Pillar 3 regulation is just the start of the EU’s requiring machine-readable sustainability-disclosure with the wider integration of the CRR3 regulation and the EBA’s overarching XBRL Taxonomy and data-point model. This change affects large credit institutions with EEA regulated market-listed securities as of December 2023, with semi-annual disclosures. Small and non-complex institutions are in scope for the CRR3 regulation starting 1 January 2025, as determined in the updated CRR3 banking package, with annual disclosures.
How does XBRL benefit institutions?
Banks using machine-readable disclosures can radically reduce the time and costs associated with key business processes such as credit analysis and monitoring and streamline their business reporting processes.
As intimated earlier, machine-readable formats allow disparate information systems to seamlessly communicate with each other, something banks have been working towards for a long time and with the advent of XBRL/XML, in particular, they have finally achieved. Even Excel can now consume XBRL information with an add-on.
The capabilities for data analysis increase exponentially with the move to machine-readable disclosure formats.
What are the technical challenges created by machine-readable formats like XBRL?
- Data-quality issues: XBRL requires the accurate and consistent tagging of financial information. Companies face challenges ensuring data quality, including inconsistent mappings, incorrect tags, and missing information − all of which have carry-on effects on submitted filings.
- Training requirements: XBRL adoption necessitates a strong grasp of its concepts, taxonomies, and software tools. A lack of awareness and understanding of these factors increases the organization’s learning curve and hinders successful implementation.
- Technical challenges: XBRL integration with existing systems and workflows poses technical challenges, including software compatibility issues, infrastructure limitations, and the need for technical expertise, further complicating implementation.
What’s next for banks?
Resolving the difficulties surrounding a move to machine-readable disclosure requirements is but one more on the long list of regulatory reporting – regional flavors of Basel III/IV, capital adequacy, ESG, submission formats, etc. − with which today’s banks must contend, and seemingly all at once.
To address the technical challenges, as well as all the conflicting regulatory requirements, banks need a solution that:
- Scales and leverages data from other regulatory solutions and facilitates data validations.
- Directly maps client data to the XBRL taxonomy’s cell cubes.
- Efficiently facilitates integration with existing data inputs, software, and the XBRL taxonomy’s technical aspects in accordance with the institutions’ operating models.
