- June 2026
Regulatory reporting is undergoing a fundamental transformation. While banks have invested heavily in standard software, automation, and centralized architectures, BearingPoint’s 2025 regulatory reporting study shows that data quality remains the single most critical challenge across European financial institutions.
Against this backdrop, banks are strengthening data governance, controls, and operating models. Most institutions have implemented period-to-period reconciliations, automated validation checks, and stronger involvement of data owners. However, the study also reveals significant maturity gaps: only 18% of banks report full BCBS 239 implementation, and comprehensive data lineage documentation remains limited.
Technology is increasingly seen as an enabler to reduce manual effort and increase consistency, particularly as banks move toward centralized data architectures and expanded use of standard software. At the same time, institutions are exploring how advanced technologies can support a shift toward straight-through processes.
This expected shift – where AI and more integrated software capabilities start displacing spreadsheets – could mark a big step toward straight-through reporting. The overarching goal is to maximize the capabilities of the standard software and emerging tech and thereby eliminate the need for parallel manual workarounds.
Stefan Kauerauf, Partner at BearingPoint
Looking ahead, regulatory initiatives such as BCBS 239 and RDARR are increasingly viewed not only as compliance requirements but as strategic frameworks for sustainable data management. Upcoming developments, such as granular reporting (e.g., IReF), will further increase pressure on data quality. Banks that proactively invest in governance, architecture, and automation are better positioned to reduce rework, meet supervisory expectations, and turn regulatory data into a strategic asset.
FAQ
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What is the biggest challenge in regulatory reporting today?
The biggest challenge in regulatory reporting is data quality. Most errors originate from missing or incorrect source data, with over half of banks reporting this as the main issue. System failures and mapping errors further increase the need for manual corrections. Despite improvements, only about one-third of banks achieve resubmission rates below 5%, showing ongoing gaps.
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How can banks improve data quality in regulatory reporting?
Banks improve regulatory reporting data quality by fixing issues at the source and strengthening governance. Leading practices include direct engagement with data owners (used by 70% of banks) and centralized data quality teams. Many banks also prioritize correcting data in a central warehouse rather than spreadsheets to avoid repeated errors.
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Why is data governance critical for regulatory reporting?
Data governance is critical for regulatory reporting because it ensures clear ownership, control, and traceability of data. Weak governance remains a major issue, with over 50% of banks citing insufficient governance in source systems. Only 18% have fully implemented BCBS 239 principles, which limits transparency and slows root-cause analysis of data issues.
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Centralized vs decentralized data models in regulatory reporting—what works better?
Centralized data models perform better in regulatory reporting by improving consistency and reducing errors. Around 54% of banks use centralized data sourcing, while only 10% remain decentralized. Banks with centralized data warehouses report fewer inconsistencies and lower resubmission rates, as all reports rely on a single, governed data source.
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What is the ROI of automation in regulatory reporting?
Automation in regulatory reporting reduces manual effort and error rates, improving efficiency and scalability. For example, 74% of banks use automated validation checks to detect anomalies early. This reduces rework and speeds up reporting cycles, allowing teams to focus on analysis rather than corrections and repetitive reconciliation tasks.
