The latest BearingPoint regulatory‑reporting study paints a stark picture of the European banking landscape in 2025. Surveying 50 banks across nine countries, the research reveals that 76 % of institutions still depend on supervisory feedback to discover and correct reporting mistakes. This reliance is not a fleeting anomaly; it signals deep‑seated data‑quality and governance deficiencies that persist despite sizable investments in automation, centralized data warehouses, and advanced analytics. For compliance officers, chief financial officers, and risk managers, the findings underscore a critical paradox: sophisticated technology stacks are in place, yet the fundamental controls needed to prevent errors at the source remain unevenly applied. The study therefore raises urgent questions about where banks should focus their next wave of improvement—on tightening data ownership and lineage, or on further polishing the tools already deployed.
BearingPoint Study Reveals Supervisory Feedback as Primary Error Trigger
The 2025 Regulatory Reporting Study, conducted by management‑technology consultancy BearingPoint, gathered quantitative responses and qualitative insights from 50 financial institutions operating in nine European jurisdictions. Respondents were asked to rank the most common catalyst for report resubmissions. An overwhelming three‑quarters (76 %) named supervisory feedback—such as regulator‑issued queries or corrective notices—as the immediate trigger for fixing submitted data.
Beyond this headline figure, the survey surfaces several complementary metrics that flesh out the broader context:
- 42 % of banks cite data quality as the single biggest challenge in meeting regulatory obligations.
- 56 % point to missing or incorrect data in source systems as the leading cause of errors, while a further 50 % highlight insufficient granularity and fragmented system landscapes as aggravating factors.
- Only 18 % of institutions report full implementation of BCBS 239 principles, and a modest 24 % have extensive data‑lineage documentation that maps data flows from origin to report.
Stefan Kauerauf, Partner at BearingPoint, summed up the governance implication:
“When three in four banks still depend on supervisory feedback to trigger corrections, rather than consistently identifying issues before submission, that is a governance problem, not a technology problem.”
The study also notes that while 66 % of banks have adopted centralized data warehouses as the backbone of their reporting architecture, the resubmission rate—the proportion of reports that must be re‑filed after initial submission—remains high. Only one‑third of institutions keep resubmission rates below 5 %, indicating that the majority still grapple with frequent revisions.
Governance Gaps Persist Despite Automation and Centralization
Automation has undeniably reshaped the reporting workflow. According to the survey, 90 % of participants have instituted at least three data‑quality controls, ranging from automated validation checks to reconciliation mechanisms. Yet the same 90 % also admit to continuing reliance on spreadsheets and other manual tools for critical steps, a paradox that highlights the incomplete nature of straight‑through processing.
Key observations from the data include:
- Centralized Architecture Adoption – Two‑thirds of banks (66 %) now run their regulatory‑reporting workloads on unified data warehouses, reducing the need for point‑to‑point integrations.
- Control Landscape – While most institutions have layered controls, the depth and consistency of those controls vary. For example, automated validation rules may catch format errors, but they often cannot detect semantic inconsistencies that arise from incorrect master data.
- Spreadsheet Dependence – Despite sophisticated back‑end systems, spreadsheets remain the de‑facto tool for ad‑hoc calculations, exception handling, and final sign‑off. This manual layer re‑introduces human error and hampers auditability.
Holger May, Partner at BearingPoint, emphasized that technology alone does not guarantee better outcomes:
“The banks pulling ahead are not necessarily those with the most advanced technology. They are the ones that have gotten serious about data ownership, lineage, and governance at every level of the organization.”
In practice, this means establishing clear data‑ownership responsibilities, maintaining end‑to‑end lineage records, and embedding granular data‑quality metrics into the daily operations of front‑office, treasury, and risk functions. Without these cultural and procedural shifts, even the most automated pipelines will continue to generate errors that surface only after regulators intervene.
Emerging AI Interest Signals a Shift Toward Advanced Governance
A notable forward‑looking insight from the study is the growing appetite for artificial‑intelligence (AI) applications in regulatory reporting. Half of the surveyed banks (50 %) plan to deploy AI use cases, with a focus on three primary areas:
- Anomaly detection – Machine‑learning models that flag out‑of‑trend data points before they reach the regulator.
- Testing and scenario analysis – Automated generation of test cases to validate data transformations and model outputs.
- Knowledge management – Natural‑language processing tools that surface relevant regulatory guidance and past supervisory comments to inform reporting teams.
Although respondents describe AI adoption as being in an early stage, the intent signals a strategic pivot: banks recognize that enhancing data quality is a prerequisite for scalable, efficient reporting, especially as regulatory demands become more granular and data‑intensive. AI can complement, but not replace, robust governance frameworks; it is most effective when fed clean, well‑documented source data and when its outputs are overseen by accountable data stewards.
Key Takeaways
- 76 % of banks rely on supervisory feedback as the primary trigger for correcting regulatory‑reporting errors.
- Only 18 % of institutions report full implementation of BCBS 239 principles, and just 24 % have extensive data‑lineage documentation.
- 50 % of surveyed banks plan to deploy AI in regulatory reporting, targeting anomaly detection, testing, and knowledge‑management functions.
FinanceInsyte's Take
The study underscores that technology investments alone will not close reporting gaps; robust data ownership and governance are essential. Executives should monitor progress on BCBS 239 adoption and data‑lineage documentation while evaluating AI pilots that address root‑cause data issues. Continued reliance on supervisory feedback suggests that regulatory risk remains elevated until internal controls can consistently pre‑empt errors.
Source: Businesswire
