Steps to a New Reality: Navigating Challenges in AI-Enhanced XBRL Analysis
This guest opinion piece is the third in a series from Björn Fastabend, bringing us a regulator’s perspective alongside a wealth of experience in digital reporting. Björn is head of the XBRL collection and processing unit at BaFin, Germany’s Federal Financial Supervisory Authority, where he supervises all related activities and leads the implementation of strategic initiatives. He is also a member of XBRL International’s Board of Directors, and previously chaired our Best Practices Board.
The views and opinions expressed in this publication are those of the author. They do not purport to reflect the opinions or views of BaFin.
From possibility to practice: addressing implementation challenges
So far in this series we have explored the powerful natural partnership between XBRL and AI, and the potential to combine AI exploration with BI verification in integrated analytics workflows. We now turn to the crucial question of implementation. How do we move from compelling possibilities to practical reality while maintaining the rigorous standards that regulatory work demands?
It is an open secret that regulators are often slow to adopt new technologies. This isn’t due to laziness or inefficiency, but rather stems from the unique regulatory context in which we operate. Regulators must be cautious when making decisions. Our judgments can reach far beyond individual companies and impact whole economies.
Adopting something as fundamentally challenging and technologically disruptive as AI is therefore a daunting task. Yet we regulators cannot afford to hide our heads in the sand: as we collect and analyse increasing volumes of data, and as the entities we regulate embrace AI with enthusiasm, we must keep up. The good news is that, with a little extra effort, regulators can incorporate AI analysis with perhaps less difficulty than we fear. We need to act, and we can act.
When considering the introduction of AI in a regulatory context, it is crucial to keep our strict regulatory requirements in mind. By proactively addressing these specialized challenges from the outset we can ensure the successful integration of AI into the regulatory analysis process.
Trust and verification
For regulators planning to integrate AI into analytics processes it is essential to define clear boundaries as to when the AI is used and what the AI can do. Due to the risk of hallucinations and lack of transparency in its outputs, AI should be limited to an exploratory role and should not act as a final decision maker. As extraordinary as they seem, AI systems run on statistics, not magic. As I have stressed throughout this series, the value of AI lies in providing analysts with insights into where to focus and what to analyze, not in assuming responsibility for the complete analysis process. It is essential that these expectations for the role of AI are managed early on.
Adopting AI in regulatory contexts requires careful consideration and planning, taking into account the policy environment in place and levels of regulatory rigor required. It is essential to build confidence through transparency, and to ensure that analysts are on board throughout the process. As so often with AI, the key message is that our aim is in no way to replace human expertise but to enhance it with an expanded toolkit.
Implementation realities
Regulators commonly supervise hundreds, if not thousands, of companies and therefore have large quantities of XBRL filings to process. It is rarely practical to analyze XBRL filings individually. In order to truly integrate AI into the regulatory analysis process, XBRL filings and taxonomies are best stored in a database used by both the AI and the BI system, ensuring that the two use identical source data.
When considering technical integration, regulators should focus on optimizing their existing infrastructure rather than building entirely new systems. Database structures often need refinements to support both precise BI queries and more flexible AI exploration. The rich semantic information in XBRL taxonomies should be accessible to both systems, preserving the relationships between concepts that give the data greater meaning. Resource planning should account not just for initial implementation but also ongoing maintenance as taxonomies evolve. A practical approach is to develop standardized processes for keeping AI models aligned with taxonomy changes, ensuring consistent analysis over time.
Data quality is a crucial factor in both AI and BI analysis, and a fundamental consideration in successful AI integration. This is one area where XBRL shines, since the built-in validation checks help ensure a high level of data quality that will be reflected in the quality of analytic outputs. Regulators may nonetheless need additional quality checks specific to AI analysis. Automated quality-assessment tools can identify potential issues before they impact results, helping maintain the reliability that regulatory work demands.
Regulatory requirements
When implementing AI in a regulatory context, data sovereignty and security considerations are paramount. For most regulatory bodies, on-premise solutions represent the optimal approach, keeping sensitive financial data entirely within a secure perimeter. This is especially critical for financial and market regulators, who handle highly confidential information that could impact market stability if compromised.
On-premise AI implementations offer several key advantages for regulatory settings. They provide complete control over data storage and processing, eliminate the risk of external data transmission, and allow for customized security measures tailored to specific regulatory requirements. While some vendors promote cloud-based AI solutions for their scalability and reduced maintenance demands, these benefits rarely outweigh the fundamental need for absolute data security in regulatory contexts. Having said that, there are now a number of regulatory environments that are utilising highly secure tenanted cloud implementations. Not all countries are able, or willing to go down that path, and in every case there is a relatively complex set of legal agreements to put in place.
When setting up these secure environments, privacy and security standards must be rigorously maintained. AI systems should operate to the same high standards of data protection as traditional analysis methods, following defined policies on data access and use. Creating meaningful audit trails is particularly important, as regulators must be able to explain and justify their processes. Each step in the AI analysis workflow should be documented and traceable, allowing for examination when needed.
Appropriate governance frameworks balance innovation with necessary controls. Clear policies should delineate when and how AI can be used in regulatory processes, including explicit boundaries on decision-making authority. It is essential to note that the final decision in any regulatory process must always lie with a human. AI can, at most, be used for exploration, never for autonomous decision making. Maintaining traditional analysis capabilities alongside new AI-enhanced methods ensures that continuity is maintained and regulatory compliance obligations are not compromised.
Moving forward
The most practical approach to AI implementation is to start small with focused pilot projects. Rather than attempting a comprehensive transformation, regulators should identify specific, well-defined challenges where AI can demonstrate clear value. These might include detecting reporting anomalies in high-risk sectors or identifying patterns of non-compliance. Such targeted projects build expertise gradually while demonstrating tangible benefits.
Learning from these early experiences is crucial for subsequent expansion. It is important to implement regular reviews, examining both technical performance and integration with existing processes. Successes and challenges should be documented, providing valuable knowledge for future initiatives. As confidence grows, regulators can build on their achievements incrementally, extending proven approaches to additional datasets or analytical capabilities.
Throughout this journey, it is essential to foster constructive dialogue about the challenges we face. Open discussion of limitations and initial setbacks is not a sign of failure but a necessary part of responsible innovation. This dialogue should extend beyond our own organizations to include reporting entities and other stakeholders. International organizations like XBRL International provide valuable platforms for discussion and knowledge sharing, enabling regulators to learn from each other’s experiences.
A proactive approach to expand the possible
The integration of AI into XBRL analysis presents both significant opportunities and challenges for regulatory bodies. By addressing these challenges proactively—establishing clear boundaries, ensuring robust technical implementation, maintaining regulatory rigor, and taking an incremental approach—we can enhance our supervisory capabilities while preserving the trust and reliability our role demands.
The key lies in viewing AI not as a replacement for existing processes but as a complementary tool that expands what’s possible. When properly implemented, AI-enhanced analysis allows regulators to work more efficiently and effectively, focusing human expertise where it adds the most value while leveraging technology for initial exploration and pattern recognition. This balanced approach represents the most promising path forward for regulatory supervision in an increasingly complex financial landscape.
