What the Push for Machine Executable Regulation Means for Your Business

On average, a mid-sized company can have up to 60 employees working through constantly evolving rules, trying to keep the organisation compliant — a large organisation could have up to 180 or more.

Now, regulators are pushing back and ushering in the age of regulatory technology.

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The manual process, thus far, has largely consisted of risk compliance officers becoming aware of a new rule, working with others to clarify what the change means for their organisation, whether they need to update their own policies, which processes and people the change will impact and lastly, implementing the update. Hence, dealing with new rules and keeping up with changes have been complex, resource-consuming and error-prone.

These shortcomings presented the perfect foil for standardisation and digitisation of the financial regulatory sector.

As early as 2017, during the Tech Sprint conducted by the Financial Conduct Authority and Bank of England, calls were sent out for Model-Driven Machine Executable Regulations (MDMER).

Model-Driven, Machine-Readable And Executable Regulations (MRER)

Model-driven, machine-readable and executable regulations use AI, machine learning, computer vision and natural language processing to analyse, interpret and execute regulation.

The MRER approach to regulation answers the need to monitor and analyse thousands of legal sources in real-time. It provides financial companies with a tool to keep up with the ever-growing regulatory changes and updates. And for regulators, it supports their supervisory work.

In July 2020, the European Banking Authority announced that

“Regulations would benefit from the translation into machine-executable form. A clear benefit is the elimination of the need by institutions to interpret the legislation if not sufficiently clear, or [there is] the possibility of misinterpreting it otherwise.”

Adopting machine executable forms would bring many other benefits, including:


  • Clarity: machine executable forms are less ambiguous than a typical natural-language regulation


  • Efficiency: decrease the time it takes to update, implement and monitor regulatory enforcement


  • Reduce cost: they bring about two-fold savings — 1) immediate savings from the decreased need for expensive and time-consuming disambiguation for businesses and 2) reduce the cost of monitoring and enforcing regulations for regulators


  • Managing change: machine executable forms enable regulators to efficiently distribute changes in regulations and businesses to quickly adapt to those changes


MRER is inverting the regulatory environment. Until now, regulatory interpretation and disambiguation took place after the regulation was put forward. But now, this burden is placed with regulators at the front-end of the process — whereby, a new rule or update is announced with disambiguation and immediate machine executability.

The Push for Machine-Readable And Executable Regulations

In the past few years, various authorities and experts have talked about the future of regulatory change management, calling for standardisation and use of technology.
Regulators like the Financial Stability Board (FSB), European Banking Authority (EBA), European Council Regulation, and Financial Stability Institute (FSI) are just a handful of authorities who’ve called for RegTech.

In September 2020, the European Commission stated that “by 2024, information to be publicly released under EU financial services legislation should be disclosed in standardised and machine-readable formats.”

Businesses have little choice but to prepare for the onslaught of regulatory changes coming their way, including the disruption of the whole industry. But implementing MRER is not without its risks, such as incorrect disambiguation, opaque machine learning systems and error in versioning.

Sign up for the Cambridge RegTech: AI for Financial Regulation, Risk, and Compliance programme to understand how you can leverage AI and ML tools to prepare for the regulatory evolution and learn about the best practices for using intelligent systems.

Cambridge RegTech: AI for Financial Regulation, Risk, and Compliance Programme is delivered as part of a collaboration with University of Cambridge Judge Business School and Esme Learning. All personal data collected on this page is primarily subject to the Esme Learning Privacy Policy.


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