Guide to regulatory reporting

Regulatory reporting is the process of collecting, validating and submitting information that laws, regulations or supervisory authorities require an organization to provide. 

Depending on the relevant regulation, regulatory reporting often contains information about an organization’s:

• balance sheets and financial statements 

• documented operational risk and risk exposure 

• capital adequacy reports and solvency

• reports of cybersecurity incidents or other suspicious activities

While failure to maintain regulatory compliance can be inherently costly in its risks to an organization’s safety and soundness, failure to demonstrate regulatory compliance poses its own risks. IBM’s 2026 Cost of a Data Breach Report revealed that being found noncompliant with regulations could add more than USD 200,000 to the average cost of a security breach in fines, making regulatory reporting a crucial element of overall risk mitigation.

Automation plays a key role in modern regulatory reporting by creating defined workflows for data collection and data management. Continuous compliance, the practice of continuously and automatically monitoring systems for compliance instead of performing periodic manual audits, can boost operational efficiency for organizations with major reporting requirements. A March 2026 IDC report found compliance accounts for 22 percent of IT spending, driving the adoption of automated compliance controls.

Different regulatory bodies have different reporting standards, so the most appropriate tools for compliance efforts, transaction reporting and data governance can vary significantly. Some applications, such as IBM’s Sovereign Core platform, come pre-loaded with over 200 frameworks to support the collection of evidence for regulatory reporting. Automated workflows and, in some cases, real-time reporting can help businesses break down data silos and establish a robust internal control framework for compliance with several types of authority, including:

• Laws and regulations, such as the GDPR, EU AI Act, SOX and HIPAA

• Prudential supervisory regimes implementing standards developed by the Basel Committee

• Certifiable standards, such as ISO/IEC 27001

• Independent assurance and attestation programs such as SOC 2

Regulatory reporting (and the practice of gathering related compliance evidence) also supports digital sovereignty, an organization’s ability to retain and prove control and authority over its technology systems and ecosystems, data, operations and artificial intelligence (AI) It achieves this by establishing evidence that the organization is in compliance and therefore safeguarding its control of its digital infrastructure.

Organizations establishing a regulatory reporting practice must work to establish:

• A culture of compliance that supports the reporting project

• A robust regulatory change management (RCM) practice for when governing bodies introduce new regulations

• Integration between their existing risk management strategies and the reporting process 

Additionally, financial organizations should understand how critically important regulatory reporting is for them specifically, and all organizations should be able to distinguish between regulatory reporting and performance reporting.

Organizations must make clear the extent to which business outcomes are tied to compliance and ensure employees are equipped to demonstrate compliance and identify lapses.

Depending on the regime and severity of the infringement, noncompliance can result in substantial fines, remediation obligations, restrictions on processing or business activities and reputational damage. In addition to promoting awareness of this risk, businesses must provide clear internal reporting paths for compliance and train employees to recognize lapses. Automation can help with this process by integrating regulatory reporting into existing risk management software, including continuous monitoring, validation checks and audit trails.

Training employees to understand these automated tools, and the baseline compliance requirements of each applicable standard or regulation, ensures accountability in regulator reporting.

To prepare for new regulatory reporting requirements, an organization needs to translate the text of the regulation or standard into measurable outputs and then test the reporting chain for those outputs before the regulation goes into effect.

This process is known as regulatory change management (RCM). RCM requires up-to-date knowledge of the regulatory landscape, thorough assessments of the changes’ impacts on business outcomes and workflow and a robust practice of documentation and auditing, often powered by continuous compliance and monitoring software.

By collecting information about subjects such as solvency, operational risk and security incidents, the process of regulatory reporting not only satisfies regulators’ requirements but gives a business a window onto its own risk profile.

Risk management involves identifying, assessing, treating, monitoring and communicating risks in line with an organization’s objectives and risk appetite. Regulatory reporting supports this process by supplying businesses with clear, organized information about the factors deemed most important by regulators and standards-setting bodies.

The regulatory reporting process, especially when practiced automatically and continuously, can provide early warnings about liquidity, remediation backlogs, or worsening data integrity that support enterprise risk management.

While regulatory reporting is required across many industries, financial institutions are among the most heavily regulated and therefore financial-sector regulatory reporting is of particular importance.

Financial institutions like banks and investment firms face especially strict regulatory reporting requirements because:

• They are systemically linked, meaning one faltering institution can cause major issues with cash flow and solvency across the industry

• Transparency is important for public confidence, allowing investors and stakeholders to honestly evaluate an institution’s health 

• They produce a vast amount of data that often includes personally identifiable information (PII)

Additionally, modern financial institutions are technologically complex and require detailed trails of evidence to demonstrate compliance and security in AI-powered systems.

While compliance reporting and performance reporting often draw on the same records and data, they fulfill different obligations: Compliance reporting evaluates whether an organization is in compliance with applicable regulations and standards, while performance reporting evaluates whether an organization is meeting its own benchmarks for financial performance and business efficiency.

Best practices for regulatory reporting emphasize strong integration of the reporting process with existing data management software.

A modern solution for regulatory reporting should continuously monitor the organization’s digital infrastructure for relevant data and organize it into traceable, high-quality, submission-ready forms. Among other things, this process should include:

• Support for relevant standards or regulations

• Integration with existing data and risk management systems

• Strong auditability and traceability features 

• Governed workflow and approval management

• Support for regulatory change management practices

Internal audit processes support regulatory reporting by analyzing the entire reporting chain from the source to the final report — not just the final report itself.

Internal audits should capture and review:

• Initial extraction from the data source

• Aggregation and validation of the data 

• Report preparation, review and approval

• The final report

Audits should answer whether the regulatory requirement is correctly understood and met, whether the data is accurate and traceable, whether data governance and review processes were followed and whether all of this is demonstrable to regulators.

Data silos are some of the most common obstacles to accurate regulatory reporting because they create inconsistent data and make it difficult for organizations to trace the data’s provenance and accuracy across the reporting chain.

Because financial institutions often face the most intensive regulatory reporting requirements, consider a bank where customer information, market and investment information and risk calculations are all stored in different platforms, with the regulatory reporting apparatus its own, separate application as well. If the same data is classified or organized differently in each section, it must be manually corrected before it is reported to regulators, a costly and potentially error-ridden process.

Large enterprises face the same basic challenges of consistency and organization that are endemic to all regulatory reporting, but their complexity increases significantly at scale. Continuous compliance practices and significant investments in data governance are key for maintaining visibility and control of regulatory data across a large organization.

Products in this market span different categories. Workiva focuses on connected reporting, assurance, controls and audit workflows, while Regnology specialises in financial-sector regulatory reporting. IBM OpenPages is primarily a governance, risk and compliance platform that supports reporting. Buyers should verify each product’s current ownership, regulatory coverage and supported connectors through vendor documentation and proof-of-concept testing.

For multinational organizations, international regulatory frameworks significantly increase the complexity of regulatory reporting.

A multinational organization might face different requirements imposed by:

• Financial reporting frameworks

• Tax regulations

• Data-protection laws

• ESG and sustainability frameworks

These reporting frameworks might require organizations to submit the same data but in different formats, at different frequencies, or with differing reporting thresholds. That poses a significant challenge for data governance, as data quality, accuracy and traceability must remain consistent despite the different reporting requirements. Frequent regulatory change only intensifies this challenge.

These challenges have led organizations to increasingly invest in automated solutions for regulatory reporting. For example, to satisfy the requirements of the GDPR a US-based bank operating in the EU might use automated data-discovery and governance tools to map personal data processing, record processing activities, identify cross-border transfers, monitor retention rules and maintain evidence that appropriate safeguards and security controls are in place. 

Artificial intelligence plays a major role in automating the labor-intensive data collection required for regulatory reporting, as well as improving data quality.

AI tools help power the practice of continuous compliance, whereby organizations scan their digital infrastructure in real-time for regulatory compliance instead of relying on periodic audits. AI can not only automatically collect and organize data, but scan the landscape for potential risks and violations, use natural language processing capabilities to analyze new regulations as part of regulatory change management and strengthen auditability by automatically creating logs and audit trails.

AI can also mitigate the risks of manual errors and data inconsistencies in regulatory reporting. By identifying:

• Unusual transactions 

• Unexpected data movements

• Reporting exceptions

• Inconsistent classifications

Automated tools can catch violations that might go unnoticed or be caused by human actors. Continuous, AI-assisted reporting can improve visibility and reduce operational and regulatory risk. 

Regulatory reporting can support digital sovereignty by providing evidence and accountability that can support sovereignty objectives.

Digital sovereignty is a framework that incorporates data sovereignty, technological sovereignty, operational sovereignty and AI sovereignty, all of which are facing a growing number of regulatory requirements. This could drive demand for more robust regulatory reporting practices that create auditable records to demonstrate compliance with data protection, privacy and governance requirements. Current sovereignty practices emphasize the ability to prove ownership and governance of data to regulatory authorities, making the audit trails and compliance documentation generated by regulatory reporting especially important.

New AI-governance requirements, including those imposed by the EU AI Act, increase the need for reliable documentation, record-keeping, monitoring and regulatory evidence. Failure to comply can expose organizations to administrative fines and other enforcement measures and, depending on the circumstances, may restrict the lawful placing or use of an AI system in the EU market.