Transforming Regulator-Institution Dynamics: The Impact of Machine-Readable Regulations

As technology continues to revolutionize industries, the concept of machine-readable regulations is emerging as a pivotal factor in enhancing compliance and bridging the gap between regulators and institutions. Emil Kongelys, the Chief Technology Officer of Muinmos, a leading RegTech company, emphasizes that machine-readable regulations will eliminate ambiguities in compliance, creating a standardized approach to regulatory frameworks.

The Future of Machine-Readable Regulations

Kongelys advocates for a unified protocol that allows all regulators to present their regulations through a common integration interface, akin to a FIX protocol for regulations. He points out that while many regulators are digitizing their frameworks, the lack of IT infrastructure is a significant hurdle in implementing a cohesive system.

• Common protocol standards are essential for regulatory clarity.
• Machine-readable regulations will facilitate immediate compliance.
• Standardization can enhance the efficiency of regulatory processes.

Trust in AI-Driven Compliance Systems

One of the critical challenges is whether regulators are prepared to trust AI-driven compliance systems. Kongelys believes that if compliance results can be fully explained, regulators will have no choice but to accept these systems. However, he notes that achieving explainability is challenging, especially with generative AI and large language models (LLMs) that operate on probabilistic outcomes.

Real-Time Data Sharing and Accountability

The advent of real-time data sharing could redefine accountability in regulatory compliance. Kongelys asserts that for this to work, the sector must adopt a uniform protocol that minimizes interpretation discrepancies among regulators. He acknowledges that while some variations may always exist, a high-level common protocol is crucial for setting accountability expectations.

End-to-End Automation in Compliance

Mark Shead, from Regnology, emphasizes that the cost of regulation extends beyond routine operations to include managing changes. He advocates for automation in regulatory processes, suggesting that if new regulations can be coded for automatic ingestion and assessment, the cost of compliance could significantly decrease.

• Automation can identify and fill compliance gaps.
• End-to-end automation could streamline reporting processes.
• Structured reporting formats like XBRL are vital for machine-readable compliance.

Challenges and Opportunities in Machine-Readable Regulations

Despite advancements, there are significant hurdles to overcome for machine-readable regulations to become mainstream. Shead points out that the existing regulatory landscape is vast and complex, requiring extensive effort to standardize both reporting outputs and the underlying data.

The Role of Standardized Data

Alex Mercer from Zeidler Group highlights that creating machine-readable rules can reduce reliance on intermediaries in compliance processes. However, challenges remain, particularly concerning the precision required in legal drafting, where even minor punctuation changes can alter meanings.

Madhu Nadig, co-founder of Flagright, believes that achieving machine-readable rules will hinge on overcoming the lack of standardization and addressing legacy system challenges. He suggests that establishing trust will require RegTech platforms to demonstrate transparency and traceability.

The Importance of Consistent Data in Compliance

Darragh Hayes, CEO of LEI, underscores that the success of machine-readable regulations depends heavily on the use of standardized and reliable data. He points to the European Union’s Digital Operational Resilience Act (DORA) as an example where a globally recognized identifier like the Legal Entity Identifier (LEI) is preferred over multiple identifiers that complicate the regulatory process.

Key Takeaways:

• Standardization is essential for effective machine-readable regulations.
• Automation can significantly reduce compliance costs and improve efficiency.
• Building trust in AI-driven compliance systems is a gradual process.

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