Data Quality Red Flags in COREP FINREP Submissions and How to Fix Them

Introduction

For European financial institutions, regulatory reporting has evolved from a standard periodic check-box exercise into a high-stakes demonstration of data integrity. Under the European Banking Authority (EBA) framework, COREP (Common Reporting) and FINREP (Financial Reporting) serve as the bedrock of prudential supervision. Together, they require institutions to submit thousands of granular data points regarding capital adequacy, risk exposures, and financial performance.

However, because these two frameworks look at a bank’s operations from different angles: COREP through a strict lens and FINREP through an accounting lens, maintaining absolute alignment is a notorious operational challenge. A single structural error or manual override can trigger automatic algorithmic rejections, erode regulatory trust, and ultimately lead to costly investigations or capital add-ons.

This blog breaks down the growing regulatory pressure on banks, identifies the top data quality red flags that instantly trigger regulatory warnings, and provides a clear blueprint to fix and prevent them before submission day.

The Growing Pressure on Banks to Improve Regulatory Data Quality

Regulatory reporting has become significantly more complex for banks in recent years. With evolving frameworks, banks are now required to manage larger volumes of increasingly granular data across COREP & FINREP submissions.

According to the EBA, supervisory reporting requirements across Europe have grown so extensively that consultants recently proposed a major simplification initiative to reduce reporting data points by nearly 50%.[1]

At the same time, regulators are using more advanced validation mechanisms, automated consistency checks, and integrated reporting frameworks to assess data quality more rigorously than ever before. Even minor inconsistencies across reports can now trigger validation failures, supervisory scrutiny, or historical resubmission requests.

This regulatory pressure is exposing the limitations of spreadsheet-driven reporting, disconnected systems, and manual reconciliations. As a result, banks are increasingly investing in automated platforms for centralized data governance and integrated regulatory reporting platforms to improve reporting accuracy and transparency.

Top 4 Data Quality Red Flags

The Blueprint: How to Fix and Prevent These Red Flags

1. Inconsistent Data Across COREP & FINREP Reports

How to Fix it: Banks need to establish a centralized regulatory data framework where all the reporting teams work from a “Single Source of Truth.” Implementing automated reconciliations between COREP and FINREP templates can help identify inconsistencies before submissions.

Prevention Strategy:

• Standardize data definitions across departments
• Eliminated siloed reporting environments
• Introduce integrated reporting platforms with cross-report validation capabilities
• Maintain consistent mapping logic across reporting templates

This ensures that reported figures remain aligned across prudential and financial reporting submissions.

2. Validation Rule Failures and Last-Minute Errors

How to Fix it: Institutions should implement automated pre-validation engones that check submissions against latest EBA validation rules before filing. This helps identify rule breaches early in the reporting cycle rather than during the final submission windows.

Prevention Strategy:

• Automated EBA taxonomy and rule updates
• Run continuous validation checks throughout the reporting process
• Create exception management workflows for faster issue resolution
• Reduce manual interventions during final reporting stages.

EBA continues to revise and update reporting validations, making proactive validation controls essential for accurate submissions.

3. Negative Values in Non-Negative Fields (Signage Errors)

How to Fix it: Banks should strengthen ETL controls and automate signage validations within their data transformation layer to ensure values comply with XBRL taxonomy requirements before report generation.

Prevention Strategy:

• Configure automated positive/negative value checks
• Standardize transformation logic across reporting systems
• Embed taxonomy-based validations into reporting workflows
• Minimize manual overrides and spreadsheet-based adjustments

Automated controls significantly reduce the risk of incorrect signage disrupting regulatory aggregation and submission processes.

4. Lack of Data Lineage and Auditability

How to Fix it: Banks must implement end-to-end data lineage frameworks that allow every reported figure to be traced back to its original source system, transform process, validation step and approval workflow.

Prevention Strategy:

• Establish centralized audit trails
• Maintain version-controlled reporting workflows
• Automate data lineage tracking across systems
• Strengthen data governance and ownership policies

Regulators expect transparent reporting processes; by improving auditability and traceability, banks can reduce compliance risks while building greater confidence in regulatory reporting accuracy.

Conclusion

As regulatory reporting requirements continue to evolve, data quality is becoming one of the biggest indicators of bank’s operational resilience and compliance maturity. Issues such as inconsistent reporting, validation failures, signage errors, and weak audit trails are no longer minor operational gaps, they are regulatory risk indicators.

The good news is that these challenges are preventable.

Banks that invest in a good RegTech solution with centralized data governance, automated validation, integrated reporting workflows and stronger auditability frameworks are far better positioned to meet growing COREP and FINREP expectations with confidence.