{"id":10652,"date":"2024-10-16T15:06:41","date_gmt":"2024-10-16T15:06:41","guid":{"rendered":"https:\/\/fintech.global\/regtech100\/?p=10652"},"modified":"2024-10-16T15:06:41","modified_gmt":"2024-10-16T15:06:41","slug":"the-key-challenges-for-machine-readable-and-executable-regulation","status":"publish","type":"post","link":"https:\/\/fintech.global\/regtech100\/the-key-challenges-for-machine-readable-and-executable-regulation\/","title":{"rendered":"The key challenges for machine-readable and executable regulation"},"content":{"rendered":"

As the age of digitalised regulation comes closer into the view, there is growing discussion around the role of machine-readable and executable regulation in the financial industry.<\/strong><\/p>\n

According to Michael Thirer, legal director of\u00a0Muinmos<\/a>, the role of such a technology may very easily grow beyond what its users could have imagined.<\/p>\n

He said, \u201cIn Greg Bear\u2019s classic sci-fi novel, \u201cBlood Music\u201d, \u00a0a renegade biotechnologist creates simple biological computers, which, in a matter of days, grow, multiply and evolve into a massive, advanced civilization, surpassing anything the biotechnologist could have imagined.<\/p>\n

\u201cThe conversation around machine-readable and executable regulation may very well resemble this turn of events. After all, in its onset, the idea behind machine-readable and machine executable regulation was to \u201ctranslate\u201d \u201chuman\u201d regulation into terms machines can understand. Now, with the rise of the Natural Language Models, it seems this \u201ctranslation\u201d may no longer be necessary, though it will be necessary to maintain the principles of transparency, explainability, accountability.\u201d<\/p>\n

However, Thirer explained that even if a direct translation of the regulation from legal code to computer code may soon not be needed, discrepancies between regulatory terms and regimes need to be settled in order to create a workable machine executable regime on a global scale.<\/p>\n

\u201cThe challenge to that may arrive from a very surprising direction \u2013 the regulators themselves,\u201d continued Thirer. \u201cThis, due to the fact that whereas regulators have really stepped up in terms of technical standards and uniformed reporting in recent years; the current trend in global regulation is \u201cAgile Regulation\u201d, which is closer, in nature, to norms-setting than descriptive regulation.\u201d<\/p>\n

Thirer said that regulators\u2019 push towards Agile Regulation \u2013 such as the UK\u2019s Consumer Duty \u2013 derives from their desire to support innovation in the field of RegTech.<\/p>\n

\u201cIronically, this trend might make it harder to develop this specific sector of RegTech, as regulation will be much less descriptive and therefore much less suitable for machine like decisioning and actioning,\u201d he added.<\/p>\n

Venky Yerrapotu, CEO and Founder\u00a04CRisk.ai<\/a>, commented that regulatory, risk and compliance processes\u00a0<\/span>receive critical input from constantly evolving regulatory, business and external risk environments. These inputs, he states, are typically unstructured content and require significant human capital to discover, analyse, and provide insights to teams and stakeholders to aid in decision-making.<\/span><\/p>\n

\u201cHowever, with new technologies, unstructured content can be digitized to be machine-readable, and further, AI techniques and models can be leveraged to discover, analyze, provide insights, to reduce risk and show compliance. To battle the much-discussed challenges that public domain Knowledge Bases present for security (hallucinations, data poisoning, bias and privacy violations),\u00a0 SLMs (small language models) are becoming the route for enterprises that want to use this technology.<\/p>\n

\u201cSMLs leverage a combination of AI techniques, engineering algorithms, and a deep understanding of the risk and compliance domain, and can be trained explicitly on regulatory, risk and compliance data for accuracy and efficiency.\u00a0 This protects data with zero-trust security and reduces bias. However, digitization will only get you 80% of the way there; Human-in-the-loop is still required by SMEs from legal, regulatory affairs, compliance and risk to review and ensure the right actions are taken by the right people,\u201d said Yerrapotu.<\/p>\n

Challenges and opportunities<\/strong><\/p>\n

Echoing a similar sentiment to Thirer,\u00a0Flagright<\/a>\u00a0growth manager Joseph Ibitola emphasised that machine-readable and executable regulation is a concept that feels straight out of a futuristic novel. However, he said its \u2018very much a part\u2019 of today\u2019s compliance conversations.<\/p>\n

\u201cThe idea is simple in theory, regulations written in a format that machines can interpret and execute automatically. It sounds like the Holy Grail of compliance, but as always, the reality is a bit more complicated,\u201d he said.<\/p>\n

What are some of the challenges? Ibitola noted that one of the first is the issue of standardisation. \u201cRegulations aren\u2019t uniform, they vary by jurisdiction, industry, and even specific use cases. Translating these human-made rules into something a machine can consistently understand and act upon is no small feat. One misinterpretation could have serious consequences, especially in highly regulated industries like finance,\u201d he stated.<\/p>\n

He continued, \u201cThen there\u2019s the trust factor. For this technology to really take off, companies need to trust that it will execute regulations as intended\u2014without overcomplicating or oversimplifying. And that\u2019s a tall order. There\u2019s always the risk of over-relying on machines, potentially losing the human judgment that often plays a critical role in interpreting the grey areas of regulatory frameworks.\u201d<\/p>\n

Despite these challenges, the Flagright growth head said the potential upside is \u2018massive\u2019. With the reduction of manual oversight, machine-readable regulation could slash compliance costs and free up teams to focus on higher-level strategic tasks.<\/p>\n

\u201cImagine a system that adapts in real-time to regulatory changes, ensuring compliance without constant manual updates. It would level the playing field, especially for SMEs, making compliance more accessible and less resource-intensive,\u201d said Ibitola.<\/p>\n

\u201cIn the long run, machine-readable regulations could lead to a more consistent application of rules, removing the subjectivity that sometimes clouds human judgment. It\u2019s a transformative vision, and while there are hurdles to overcome, those who embrace this shift early will likely find themselves ahead of the curve,\u201d he finished.<\/p>\n

Ascent<\/a>\u00a0emphasised their view that key challenges to machine readable and executable regulation lies in the regulation itself.<\/p>\n

\u201cRegulation stipulates that the firm is the ultimate sign off on compliance. What happens if the machine reads and applies an obligation or a change incorrectly leading to non-compliance?\u00a0 Who is at fault? If it is the Regulatory Body who \u2018own\u2019 the model that reads and generates the executable code \u2013 then are they at fault for everyone\u2019s non-compliance?\u00a0 How would this play out?\u00a0 Personally, machine executable regulation poses significant challenges for the industry as a whole, not just for individual firms,\u201d the firm said.<\/p>\n

Ascent continued, \u201cNo matter what the future holds for machine-readable and executable regulation, it won\u2019t be practical to use and implement unless we can settle on a global standard for how this data is represented. Ideally through an independent, non-profit consortium, similar to how the W3C that enabled the explosive growth of the internet and Web 2.0. Without a single standard, adoption and innovation will remain impractical.\u201d<\/p>\n

An addition to compliance<\/strong><\/p>\n

RegTech firm\u00a0RelyComply<\/a>, meanwhile, exclaimed that MRR and MER technology is a \u2018handy\u2019 addition to compliance that can remove the timely, costly middleman of interpreting a legal document.<\/p>\n

The company said, \u201cIn financial regulations, to get a machine to understand and execute an institution\u2019s requirements, the FCA guide lowers or removes the errors associated with manual oversight in a fraction of the time. The technology has even greater efficiency benefits: reducing costs, increasing interpretation accuracy and swift implementation.<\/p>\n

\u201cBut for every upside, there are downsides. Much like the financial industry faces challenges using machine learning for investigative purposes, the capabilities of such advanced tech require the knowledge of experts who can best put it into practice. AI can hallucinate or contextualise data based on trained biases. Similarly, the fundamental task of MRR and MER to make compliance information as clear as possible could result in incorrect disambiguation or removal of flexible trains or thought.\u201d<\/p>\n

Ultimately, using them would require a big cultural shift in interpreting regulatory requirements and relying on the trust in the accuracy of machines, said the South African RegTech.<\/p>\n

\u201cBefore MRR\/MER methods can scale, adoption must be accepted everywhere. As with many strands of AML compliance, the difficulty remains that differing jurisdictional rules and processes stand in the way of technology that standardises tricky regulations. Correct training for MRR and MER would need to be rolled out, audited and fine-tuned industry-wide to assess its accuracy with prominent regulatory documents.<\/p>\n

\u201cA form of standardisation in executing regulation is welcome; after all, MRR\/MER can help extract only pertinent information to close the gap between compliance lawmaking and its widespread execution. Hopefully, its prominent RegTech advantages will allow it to be prioritised for use by compliance professionals, and it will start to bear fruit in the ever-more-difficult regulatory landscape,\u201d the business concluded.<\/p>\n

Copyright \u00a9 2024 RegTech Analyst<\/p>\n","protected":false},"excerpt":{"rendered":"

As the age of digitalised regulation comes closer into the view, there is growing discussion around the role of machine-readable and executable regulation in the financial industry. According to Michael Thirer, legal director of\u00a0Muinmos, the role of such a technology may very easily grow beyond what its users could have imagined. He said, \u201cIn Greg […]<\/p>\n","protected":false},"author":4,"featured_media":10653,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[49],"yoast_head":"\nThe key challenges for machine-readable and executable regulation - RegTech100<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fintech.global\/regtech100\/the-key-challenges-for-machine-readable-and-executable-regulation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The key challenges for machine-readable and executable regulation - RegTech100\" \/>\n<meta property=\"og:description\" content=\"As the age of digitalised regulation comes closer into the view, there is growing discussion around the role of machine-readable and executable regulation in the financial industry. 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