Regulatory Reporting in 2025: A New Era of Standardization

Jump to: Regulatory reporting | Core data management challenges | ‘Standardization’ of regulatory reporting architecture | Looking ahead

In 2023, Chartis and Regnology collaborated on the theme of bringing back tech to the RegTech universe, spotlighting the transformative role of technology in compliance. Our 2024 collaboration on the cost of regulatory reporting offered a practical framework to help financial institutions assess their compliance expenditure while modernizing their regulatory reporting infrastructure.

In 2025, Chartis and Regnology offer a new collaboration, sharing more timely perspectives on the evolving regulatory reporting landscape. This year’s focus areas are the current state of the regulatory reporting market, the persistent data management challenges faced by institutions, and the imperative for standardized regulatory reporting architectures for both regulated entities and regulators.

We also provide our perspective on the varying levels of regulatory certainty across jurisdictions and explore how these dynamics are accelerating the shift toward structured, scalable and resilient data architectures. Our analysis spans a wide spectrum of financial institutions, including tiered banks, credit unions, challenger banks, broker-dealer firms and insurance companies. We offer insights into how each is adapting to regulatory complexity in an era defined by data, digitalization and strategic transformation.

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Regulatory reporting: at the crossroads of geopolitics, economics and digital transformation

Since the 2008 global financial crisis, the banking and financial services industry has experienced a steady expansion in regulatory obligations and associated reporting requirements. A similar regulatory surge followed the 2020 pandemic, catalyzing developments across domains such as liquidity risk; environmental, social and governance (ESG); climate risk; artificial intelligence (AI) governance and prudential frameworks, including Basel III/IV, the Fundamental Review of the Trading Book (FRTB) and International Financial Reporting Standard (IFRS) 9. A clear industry trend has emerged: a shift toward more granular, data-intensive and increasingly real-time reporting.

Today, however, the geopolitical and economic landscape is exerting unprecedented influence on how regulatory reporting frameworks evolve globally. Inflationary pressures, energy insecurity, shifting geopolitical power dynamics and the resurgence of deregulatory sentiment, especially in the US, are all shaping what financial institutions are required to report, and how quickly they must do so.

Until recently, many market participants expected that regulation in areas such as AI, climate risk and ESG would continue to tighten and converge globally. But current market signals suggest otherwise. The pace of implementation of these emerging regulations has slowed, driven by political shifts, regulatory fatigue and lobbying from financial and industry stakeholders.

Chartis and Regnology believe that this marks a structural shift in the regulatory landscape, one with implications that could last through the decade. While the US is clearly leading the deregulatory charge, it is not alone. Changes in the US are likely to trigger lobbying pressure in other regions, particularly the European Union (EU), where there will be mounting resistance to regulatory asymmetry that disadvantages local institutions.

Still, Europe (including the UK) remains one of the most mature regulatory environments globally, where regulated entities are well-versed in a broad spectrum of regulatory regimes. These include financial reporting, statistical and prudential disclosures, IFRS 9 compliance, and transaction and tax reporting, among others. However, even within Europe, signs of pushback are evident. These include the postponement of the European Central Bank’s (ECB’s) Integrated Reporting Framework (IReF) to 2029, which reflects hesitancy in harmonizing the ECB’s statistical frameworks across all EU member states.

Meanwhile, the Middle East and North Africa (MENA) and Asia-Pacific (APAC) markets continue their progress toward maturity, with regulators such as the Monetary Authority of Singapore (MAS), the Australian Prudential Regulation Authority (APRA) and the Hong Kong Monetary Authority (HKMA) increasingly adopting data-driven, digitally enabled regulatory architectures. Many of these jurisdictions are aligning with European frameworks, using them as benchmarks in designing and scaling their own supervisory models.

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Core data management challenges

For regulated entities and regulators

Effective regulatory data management is foundational to the success of the regulatory reporting ecosystem – yet remains one of its most complex challenges. Both regulated entities (such as banks, broker-dealers and insurers) and regulators (such as central banks and supervisory authorities) face similar yet distinct hurdles in sourcing, transforming, validating and consuming regulatory data (see Table 1).

Challenges and issues exist throughout the regulatory reporting landscape. On the regulated entities’ side, these include:

• Data sourcing across various upstream systems.
• Aggregating the data.
• Performing regulatory data validations.
• Managing data workflows.
• Developing logical data models based on the type of reporting for a given jurisdiction.
• Performing regulatory calculations (including scenario analysis and stress testing).
• Complying with BCBS 239 and equivalent frameworks around data governance and data lineage.
• Generating reports and submitting them to regulators.

Regulators have similar challenges in collating data from various regulated entities, performing data validations, aggregating data and consuming it for analysis.

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The ‘standardization’ of regulatory reporting architecture

What ‘standardization’ means

For regulated entities

Standardization refers to the adoption of flexible, automated and scalable RegTech architectures that can:

• Manage complex, multi-jurisdictional compliance requirements.
• Enable consistent data models, reporting workflows and technology frameworks across entities and geographies.
• Improve data quality, reporting efficiency and regulatory engagement.
• Facilitate traceability and governance in line with principles such as BCBS 239 and equivalent frameworks.
• Support scalability as regulations evolve.

For regulators

Standardization means building robust and scalable SupTech systems that can:

• Ingest data from diverse regulated entities in uniform formats and harmonized taxonomies.
• Perform automated validation, data aggregation and analytics more effectively.
• Enable the comparability, timeliness and quality of reported data across the financial ecosystem.
• Streamline supervisory processes and improve decision-making with data-driven oversight.

Why is ‘standardization’ required?

Chartis and Regnology strongly believe that the key forces behind the goal of standardizing the regulatory reporting architecture are:

• Data quality and traceability. Standardized architectures help ensure accurate, data-governed reporting that complies with BCBS 239 and equivalent frameworks.
• Regulatory expectations. Authorities across the globe (like the ECB, EBA, PRA, Fed, MAS and APRA¹) are pushing for consistent, granular, high-quality data.
• Risk management. Standardized architectures enable more integrated views of exposures and systemic risk.
• Cost and efficiency. Standardized architectures reduce the duplication of reporting efforts across different teams and regulatory requirements.
• Faster onboarding of new regulations. Modeling the data logically saves considerable time when onboarding newer regulations and deriving their reporting requirements.
• Strategic data reuse. A golden source of derived data enhances the performance of advanced analytics and stress testing.
• Easier adoption of advanced technologies (such as AI, machine learning [ML] and robotic process automation [RPA]) to achieve an automated regulatory reporting workflow on top of standardized regulatory data management processes.

‘Next-gen’ standardized regulatory reporting architecture

The future of regulatory reporting lies in an integrated, end-to-end digital ecosystem that connects regulated entities and regulators through a unified, intelligent and adaptive architecture that leverages elements such as shared taxonomies, real-time data exchange and AI-enhanced supervision (see Figure 1).

Chartis and Regnology have identified the following key components as essential to building this future-ready, standardized architecture:

• Regulatory policies are a foundational element of the regulatory reporting lifecycle. They are formal directives issued by regulators that define the parameters under which financial institutions must report data. These policies govern several critical aspects:
  • How should data be modeled and interpreted? Regulatory data point models, taxonomies and messaging structures linked to policies and guidelines.
  • What data must be reported? Regulatory guidelines define the scope of financial, risk and operational data that financial institutions are required to capture and disclose.
  • How must it be calculated? Financial institutions must perform regulatory calculations in line with prescribed methodologies, including capital adequacy, credit risk, liquidity metrics and other prudential ratios.
  • What format must be used for submission? Regulatory authorities specify standardized submission templates and formats, such as XBRL, XML and CSV, ensuring consistency, comparability and automation.
  • When must it be submitted? Reports must be compiled, validated and submitted within predefined frequencies (e.g., daily, monthly, quarterly, etc.), as mandated by regulators.
    
    Investing in a regulatory reporting system that natively embeds and adapts to regulatory policies is a critical priority for financial institutions, for several reasons: to ensure financial stability, promote financial transparency and achieve market confidence, to enable strategic planning for capital allocation, to ensure adequate risk mitigation and compliance with regulatory rules to avoid penalties, and to ensure a robust data governance and lineage framework.
• RegTech and SupTech platform architectures, based on similar principles, to bridge the data management gaps between regulated entities and regulators. This should also help regulators consolidate data across entities and perform macroeconomic analyses.
• Cloud-native infrastructure deployment, by both regulated entities and regulators, to help firms scale and automate models, and develop strong capabilities around analytics.
• Regulatory application programming interfaces (APIs) to achieve real-time submission via shared taxonomies.
• Unified data models (such as the Banks’ Integrated Reporting Dictionary [BIRD], Data Point Model [DPM] and ISO 20022) for data consistency.
• Data lineage tools to ensure traceability from source to submission.
• Metadata management to help firms tag and reuse regulatory data for improved efficiency.
• AI/ML analytics layer for predictive supervision and stress scenario modeling.

Regulatory certainty vs. a shift toward ‘standardized’ architectural thinking

The level of regulatory certainty is a critical factor influencing how regulated entities implement standardized regulatory reporting architectures. In a world where regulatory landscapes are becoming more volatile due to geopolitical influence, economic cycles and technological shifts, regulated entities are evolving their data and reporting strategies toward structured and scalable regulatory reporting architectures. Standardized regulatory reporting frameworks are becoming a strategic asset and not just a compliance-related cost. Regulated entities that invest early in architectural maturity frameworks are more resilient to future regulatory shifts and better aligned with supervisory expectations.

Chartis and Regnology have jointly researched and studied the banking market across industry tiers, including challenger banks, credit unions, co-operative banks, broker-dealer institutions and insurance companies in the North American, European (including the UK), MENA and APAC markets. Figure 2 and Table 2 provide a snapshot of the regulatory maturity of the various types of regulated entity, and the strategies they are adopting as they move toward ‘standardized’ regulatory reporting architectures.

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Looking ahead

In the forthcoming articles in this series, we will explore targeted perspectives on the progression of standardized regulatory reporting data architectures across banks and broker-dealer institutions. Each article will focus on key geographic markets, including North America, Europe (including the UK), MENA and APAC, highlighting their current data infrastructure, regulatory priorities and progress toward standardized, scalable and agile reporting architectures.

Note

1. European Central Bank, European Banking Authority, Prudential Regulation Authority, Federal Reserve System, Monetary Authority of Singapore, Australian Prudential Regulation Authority

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