In today’s rapidly evolving regulatory landscape, banks are facing a fundamental shift: the modelling reset. Driven by initiatives like DPM 2.0, BIRD, and IReF, this transformation is redefining how institutions source, structure, and report regulatory data. Far from being a technical adjustment, it marks a strategic pivot; one that requires banks to rethink their data architecture, governance, and compliance frameworks. This article explores how banks are navigating this reset, the implications for regulatory reporting, and technologies enabling a more resilient, model-driven future.
Why everything is a model: Understanding DPM 2.0, BIRD, and IReF
Three core initiatives are redefining how banks approach regulatory reporting. Each plays a distinct role in establishing a model-driven framework that supports consistency, automation, and data quality across the euro area. Together, they form the foundation for a more resilient and harmonized reporting ecosystem
- The BIRD framework sets the stage for data sourcing, outlining how granular source-level data should be meticulously gathered and semantically defined. With its emphasis on detail, BIRD empowers subject matter experts (SMEs) to contribute their insights effectively, fostering a deeper understanding of data context and quality.
- Alongside BIRD, the IReF initiative introduces a harmonized model for statistical reporting, ensuring consistency in input requirements across countries in the euro area.
- In parallel, DPM 2.0 serves as the meta-model, crafting not only the structure of supervisory reporting templates but also essential validation rules, transformation logic, and data transmission formats.
A noteworthy aspect of BIRD and IReF is their alignment in modelling guidelines and naming conventions, creating a shared semantic backbone and a common language for regulatory data. This collaborative framework enables institutions to synchronize their internal data flows with regulatory requirements, fostering uniformity in both statistical and supervisory reports.
DPM 2.0 enhances this foundation by offering a machine-readable architecture that clearly articulates how regulatory needs are conveyed and implemented. It standardizes data points, business rules, and transformation logic, effectively bridging the gap between theoretical concepts and the practical generation of reports.
However, embracing modelling goes beyond technicalities; it serves as a pathway to reducing regulatory burdens by harmonizing industry practices and reported figures. While effective modelling lays the groundwork for end-to-end automation, its true potential is only realized when institutions invest in data quality, integration, and governance. Engaging SMEs in this process ensures that the insights needed for accurate data interpretation are consistently integrated, enhancing the overall reliability of reports.
BIRD as a strategic framework for granular data
BIRD is positioned as a strategic reference model that provides guidance for banks to structure and transform regulatory data, from the source to the report, through consistent logic and semantics. It advocates for key architectural principles, including the organization of granular, source-aligned data and the application of modelling logic across various regulatory domains, ensuring clear traceability from raw data through to final reports. Although participation in BIRD is voluntary, the aim is to equip banks with the means to navigate regulatory changes more smoothly, minimize interpretation efforts, and promote consistency across different reporting frameworks.
BIRD shares similar architectural objectives with the OneSumX Data Foundation, both striving to reduce fragmentation, eliminate manual mapping, and enhance clarity through semantic consistency. By adopting this modelling logic, banks not only streamline compliance processes but also lay the groundwork for a scalable and resilient regulatory reporting environment.
With OneSumX for Regulatory Reporting, financial institutions put themselves in a strong position to prepare for and align with initiatives like BIRD, IReF, and DPM 2.0, reducing complexity and enhancing long-term compliance readiness.
IReF: The mandatory shift in statistical reporting
In contrast to BIRD’s voluntary nature, IReF represents a mandatory initiative designed to fundamentally alter how banks report statistical data across the euro area. By consolidating existing ESCB statistical collections into a unified model, IReF introduces a granular, BIRD-aligned data layer with unified definitions and formats across jurisdictions. This marks a significant transformation, moving beyond mere reporting adjustments to a comprehensive re-engineering of statistical data sourcing, structuring, and validation processes.
For banks, adapting to IReF is essential, and those who embrace it early will be better poised to enhance compliance, reduce manual workloads, and effectively respond to future regulatory shifts.
By adopting IReF in advance, banks can establish a robust reporting framework that meets regulatory expectations and is resilient enough to support daily operations.
As industry leaders, Wolters Kluwer contributes to the testing program for defining the IReF data model and is involved with both the IReF and BIRD projects.
Benefits and challenges of granular data models
The move towards detailed, source-level data has become a hallmark of the European Central Bank's model-driven approach to regulatory reporting. Institutions are now called to capture data at its most fundamental granularity rather than relying on aggregated summaries. This shift not only enhances traceability and consistency but also enables the reuse of data across diverse reporting frameworks. While the advantages are clear, the transition does present challenges.
To meet these new requirements, banks must evolve their data platforms and pipelines, incorporating robust technological solutions capable of handling the complexities of modern data demands. Technologies should not only facilitate data collection but also support stringent data quality measures, ensuring that insights derived from the data are reliable and actionable. Without such advancements, the aspirations of achieving seamless automation, quick reconciliation, and responsive regulatory reporting could remain unfulfilled.
Technology, automation, and AI in regulatory reporting
As the regulatory environment evolves, the integration of technology, automation, and AI becomes essential for achieving compliance efficiencies. Effective modelling not only lays the groundwork for end-to-end automation but also enhances opportunities for institutions to utilize AI capabilities for analyzing complex data patterns. Investing in advanced data management platforms enables banks to implement automated processes for data validation and anomaly detection, significantly improving the quality of reported data.
By streamlining these operations, institutions can respond more quickly to regulatory demands and ensure ongoing compliance. Moreover, the adoption of AI technologies supports deeper insights into data management, allowing institutions to make informed decisions quickly and accurately. This capability is crucial as regulatory requirements continue to shift, reinforcing the need for robust technological frameworks that can facilitate seamless adaptability.
Embracing the future: Building a resilient reporting ecosystem
Ultimately, the move towards a model-driven approach transcends merely adhering to new regulatory standards; it signifies a commitment to creating a resilient, future-forward data ecosystem. By leveraging automation and AI, banks can enhance their reporting processes and build a framework capable of adapting seamlessly to future regulatory demands. To thrive in this environment, institutions must invest in sophisticated data management solutions that integrate these technologies, ensuring data quality and operational efficiency.
As organizations navigate this complex landscape, OneSumX stands out as an essential partner in regulatory reporting and compliance. With its comprehensive suite of solutions, OneSumX empowers institutions to streamline compliance processes, enhance data quality, and leverage automation and AI for more efficient reporting.
