As the era of digitalized regulation approaches, debate is intensifying around the role of machine-readable and machine-enforceable regulation in the financial sector.
According to Michael Thirer, legal director of Muinmosthe role of such technology could very easily expand beyond what its users could have imagined.
He said: “In Greg Bear’s classic science fiction novel, ‘Blood Music,’ a renegade biotechnologist creates simple biological computers that, within days, grow, multiply and evolve into a massive, advanced civilization, surpassing everything the biotechnologist can imagine. could have imagined.
“The debate around machine-readable and machine-executable regulations could very well resemble this turn of events. After all, the original idea behind machine-readable and enforceable regulations was to “translate” “human” regulations into terms that machines could understand. Today, with the rise of natural language models, it seems that this “translation” is no longer necessary, although it will be necessary to maintain the principles of transparency, explainability and accountability.
However, Thirer explained that while a direct translation of regulation from legal code to computer code is soon no longer necessary, discrepancies between terms and regulatory regimes must be resolved in order to create a machine-executable regime at the same time. global scale.
“The challenge could come from a very surprising direction: the regulators themselves,” Thirer continued. “This is because while regulators have really stepped up their efforts in terms of technical standards and uniform reporting in recent years; The current trend in global regulation is “agile regulation”, which is, in nature, closer to standard-setting than descriptive regulation. »
Thirer said regulators’ push for agile regulation – such as Consumer Duty in the UK – stems from their desire to support innovation in RegTech.
“Ironically, this trend could make it more difficult to develop this specific sector of RegTech, as regulation will be much less descriptive and therefore much less suited to automated decisions and actions,” he added.
Challenges and opportunities
Echoing a similar sentiment to Thirer, Flag right Joseph Ibitola, head of growth, noted that machine-readable and machine-enforceable regulation is a concept that seems straight out of a futuristic novel. However, he said it “is an integral part” of today’s conversations about compliance.
“The idea is simple in theory: regulations written in a format that machines can automatically interpret and execute. This sounds like the holy grail of compliance, but as always, the reality is a little more complicated,” he said.
What are some of the challenges? Ibitola noted that one of the first questions was the issue of normalization. “Regulations are not uniform, they vary across jurisdictions, industries and even specific use cases. Translating these human-created rules into something a machine can understand and apply consistently is no easy feat. Misinterpretation could have serious consequences, especially in highly regulated sectors like finance,” he said.
He continued: “Then there’s the trust factor. For this technology to really take off, companies need to be confident that it will enforce regulations as intended, without overcomplicating or oversimplifying. And it’s a big challenge. There is always the risk of relying too much on machines, which risks losing the human judgment that often plays a vital role in interpreting the gray areas of regulatory frameworks.
Despite these challenges, Flagright’s head of growth said the upside potential is “tremendous.” With the reduction of manual oversight, machine-readable regulations could reduce compliance costs and allow teams to focus on higher-level strategic tasks.
“Imagine a system that adapts in real time to regulatory changes, ensuring compliance without constant manual updates. This would level the playing field, particularly for SMEs, by making compliance more accessible and less resource intensive,” Ibitola said.
“In the long term, machine-readable regulations could lead to more consistent application of rules, eliminating the subjectivity that sometimes clouds human judgment. This is a transformative vision, and while there are hurdles to overcome, those who embrace this change early will likely find themselves ahead of the curve,” he concluded.
Ascent They highlighted that the main challenges for machine-readable and machine-executable regulations lie in the regulations themselves.
“The regulations stipulate that the company is the ultimate guarantor of compliance. What happens if the machine incorrectly reads and applies an obligation or change resulting in non-compliance? Who is at fault? If it is the regulatory body that “owns” the model that reads and generates the executable code, then is it responsible for everyone’s non-compliance? How would this happen? Personally, machine-enforceable regulations pose significant challenges to the industry as a whole, not just individual companies,” the firm said.
Ascent continues: “No matter what the future holds for machine-readable and executable regulations, they will not be practical to use and implement unless we can agree on a global standard on how this data is represented. Ideally through an independent, non-profit consortium, like the W3C which enabled the explosive growth of the Internet and Web 2.0. Without a single standard, adoption and innovation will remain impractical.
A complement to compliance
RegTech Company Rely on Complymeanwhile, exclaimed that MRR and MER technologies provide a “practical” complement to compliance that can remove the quick and costly middleman of interpreting a legal document.
The company said: “In the area of financial regulation, to enable a machine to understand and execute an institution’s requirements, the FCA Guide reduces or removes errors associated with manual monitoring in a fraction of the time . The technology has even greater efficiency benefits: reduced costs, increased accuracy of interpretation and rapid implementation.
“But with every advantage, there are disadvantages. Just as the financial industry faces the challenges of using machine learning for investigative purposes, the capabilities of these advanced technologies require the knowledge of experts who can best put them into practice. AI can hallucinate or contextualize data based on trained biases. Likewise, MRR and MER’s fundamental task of making compliance information as clear as possible could result in incorrect disambiguation or removal of flex trains or thoughts.
Ultimately, their use would require a major cultural shift in the interpretation of regulatory requirements and trust in the precision of the machines, South African RegTech said.
“Before MRR/MER methods can evolve, their adoption must be accepted everywhere. As with many aspects of AML compliance, the challenge remains that different jurisdictional rules and processes stand in the way of technology that standardizes sensitive regulations. Appropriate training for MRR and MER should be deployed, audited and refined industry-wide to assess its accuracy against important regulatory documents.
“A form of standardization in the execution of regulations is welcome; after all, MRR/MER can help extract only relevant information to bridge the gap between the development of compliance legislation and its widespread implementation. Hopefully, its significant RegTech benefits will allow it to be prioritized for use by compliance professionals, and it will begin to bear fruit in an increasingly challenging regulatory landscape,” the company concluded.
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