{"id":11506,"date":"2025-12-15T12:46:11","date_gmt":"2025-12-15T12:46:11","guid":{"rendered":"https:\/\/fintech.global\/regtech100\/?p=11506"},"modified":"2025-12-15T12:46:11","modified_gmt":"2025-12-15T12:46:11","slug":"how-data-can-help-navigate-a-shifting-rulebook","status":"publish","type":"post","link":"https:\/\/fintech.global\/regtech100\/how-data-can-help-navigate-a-shifting-rulebook\/","title":{"rendered":"How data can help navigate a shifting rulebook"},"content":{"rendered":"

Regulations are no longer a fixed set of rules\u2014they\u2019re a constantly shifting landscape that challenges even the most seasoned compliance teams. Staying ahead isn\u2019t just about interpreting the rulebook; it\u2019s about seeing what\u2019s coming next. Data is proving to be the tool that brings clarity to this complexity, uncovering patterns and insights that were once hidden. From advanced analytics to intelligent automation, institutions are learning to navigate change with confidence.<\/strong><\/p>\n

The financial world is rapidly evolving as new technologies grace the global stage. For\u00a0ALMIS<\/a>\u00a0CEO Luke DiRollo, this is no different in financial regulation \u2013 something he claims is \u2018putting it lightly\u2019.<\/p>\n

He explained, \u201cIt really kicked off with the major failures across the financial sector that triggered systemic consequences for individuals and businesses everywhere. Leaving aside the rather heated debates about how governments propped up failing institutions (\u201ctoo big to fail\u201d and all that), what we\u2019ve been left with is simple: global financial markets are now buried under mountains of regulation.\u201d<\/p>\n

DiRollo cited the UK banking sector, which reportedly spends \u00a333.9bn every year on regulatory compliance. \u201cThat\u2019s 13% of all operating costs \u2013 a staggering share of overhead devoted solely to understanding, implementing, and maintaining compliance,\u201d he said. \u201cSince 2020, 84% of firms say these costs have risen \u2013 in many cases \u201csubstantially.\u201d As an industry, we should be worried. This trajectory isn\u2019t sustainable. And the cost will always, eventually, hit consumers.\u201d<\/p>\n

Every piece of legislation brings its own flavours of scrutiny, said DiRollo. He added that some of us chase scientific precision; others prioritise speed and efficiency. \u201cWe\u2019ll keep debating the detail forever, but we also need to step back and consider the foundational framework \u2013 the mechanism that underpins how banks respond to regulation in the first place,\u201d he said.<\/p>\n

DiRollo remarked that regulators needs change fast, business models evolve and new products appear overnight \u2013 and regulators must respond whilst also juggling social, economic, political and technological pressures.<\/p>\n

He said, \u201cThey\u2019re struggling to keep up as it is. We\u2019ve barely touched the surface when it comes to understanding the impact of deposit aggregators or \u201cbuy now, pay later\u201d products, both of which clearly shift risk profiles. And here\u2019s a favourite example: global liquidity and funding rules still don\u2019t really consider the speed at which outflows occur in an age of instant online banking.\u201d<\/p>\n

Despite all this, he added, he believes we all know what the \u2018holy grail\u2019 looks like \u2013 granular data.<\/p>\n

\u201cBut what actually is granular data?\u201d, said DiRollo. \u201cHow does it truly move the needle? And why, after talking about it for over a decade, does it still feel like a distant utopia? Here\u2019s the candid truth: granular data means the death of \u201cRegTech\u201d as we know it. We can\u2019t keep treating regulatory reporting as a separate, siloed discipline living somewhere off to the side of the \u201creal\u201d business.\u201d<\/p>\n

DiRollo continued, \u201cGranular data means collecting, standardising and democratising information so it can serve every part of the organisation. Business decisions in uncertainty should flow from the same data. Regulatory reports should be produced from the same data. And the only thing stopping us is alignment on a standard, normalised, extensible data model that works across the business. And ideally across the sector. When you boil a bank down to its essence, this is entirely achievable.\u201d<\/p>\n

At ALMIS, the CEO outlined, it has been empowering banks with actionable insight for over 35 years. \u201cThe core needs of CFOs haven\u2019t fundamentally changed in that time. Where new regulations introduced alternative views or new aggregations, we simply extended the data model, adding customer or product attributes where necessary. This approach has massively reduced the data-collection burden,\u201d he detailed.<\/p>\n

Once the data model is consistent, reporting logic becomes distributable, he claims. \u201cWhether by us, by a community, or ideally by the regulator. It saves users huge amounts of time and effort. And because it\u2019s a model built by a community, it continues to pay dividends.\u201d<\/p>\n

A key point detailed by DiRollo is his belief that regulators and practitioners both want granular data.<\/p>\n

\u201cWe\u2019re all aligned,\u201d he said. \u201cThe only disagreement is on which data points matter. Risk-weighted assets, behavioural outflows, repricing cashflows \u2013 these are not \u201cregulatory outputs.\u201d They\u2019re fundamental data items that every bank should already hold if it wants to manage its risks properly.<\/p>\n

\u201cIf we invest in that shared, extensible data model, everyone benefits. Executives get better insight. Regulators get cleaner data and can adjusted quicker The sector gets a future-proof foundation. And maybe, just maybe, we can stop adding more weight to that ever-growing regulatory mountain,\u201d DiRollo concluded.<\/p>\n

Changing times<\/strong><\/p>\n

Areg Nzsdejan, CEO and co-founder of\u00a0Cardamon<\/a>, believes firms are increasingly treating regulation as data rather than documents.<\/p>\n

Instead of relying on manual PDF reviews, firms are now using automated pipelines to continuously ingest updates from hundreds of regulatory sources. \u201cWe structure the text into machine-readable components and apply NLP models to classify themes, detect impact, and highlight changes against previous versions,\u201d Nzsdejan explains.<\/p>\n

These insights are then routed in real time to the right owners across product, compliance, legal, and engineering, with each update mapped directly to the affected policies, controls, and business areas. The result is a transformation from slow, reactive horizon scanning to a fast, data-driven model where firms can interpret regulatory change instantly, act on it sooner, and maintain a clear audit trail without operational drag. Nzsdejan notes that \u201cthis is an enormous amount of effort, which is being enabled by companies like ourselves at Cardamon.\u201d<\/p>\n

On whether machine learning can truly capture the nuance and intent behind evolving regulations, Nzsdejan says: \u201cMachine learning can interpret regulation far better than most people expect, but it needs deep context to do so.\u201d Modern models can recognise definitions, carve-outs, thematic patterns, and even subtle shifts in supervisory expectations by analysing how language changes over time.<\/p>\n

They can compare obligations, infer likely impacts, and flag contradictions faster and more consistently than any manual review. \u201cBut intent\u2014the political, economic, and supervisory rationale behind a rule\u2014often sits between the lines,\u201d he adds. ML can surface signals and probabilities, yet humans are still needed to interpret proportionality, fairness, risk appetite, and the real expectations of regulators. In practice, the best outcomes come from \u201cpairing machine-driven analysis with human expertise overlaid.\u201d<\/p>\n

When asked about the biggest barriers preventing organisations from turning regulatory text into actionable intelligence, Nzsdejan points out that they are structural rather than technological. Regulations are \u201cdense, cross-referenced, inconsistent across jurisdictions, and often updated in fragmented ways,\u201d making them extremely hard to convert into clean, machine-readable data. Within firms, knowledge is dispersed across legal, compliance, product, engineering, and operations, with no single owner responsible for translating rules into concrete requirements.<\/p>\n

Even when teams understand the regulation, they often struggle to connect it to specific products, controls, customer segments, or processes. Without a consistent taxonomy or operating model, insights remain trapped in PDFs, summaries, or inboxes rather than flowing into workflows. \u201cMost organisations don\u2019t lack regulatory information\u2014they lack the structure, alignment, and governance to turn it into operational action,\u201d he explains. One of Cardamon\u2019s key value propositions, Nzsdejan says, is \u201cconverting legalese into actionable intelligence using our proprietary technology.\u201d<\/p>\n

Looking ahead, will regulators themselves start to rely on similar technology to oversee compliance more dynamically? Nzsdejan observes that the shift is already underway. Supervisory bodies are experimenting with AI-driven thematic reviews, automated document ingestion, market surveillance models, and more structured rulebooks that can be interpreted by technology rather than humans. Some even provide APIs to access these systems. Their goal is clear: \u201cto shift from retrospective, sample-based supervision to real-time, data-driven oversight.\u201d<\/p>\n

As financial services become faster and more digital, regulators cannot rely on periodic audits and manual reviews to keep pace. Over time, supervised firms and regulators will increasingly use similar technology stacks\u2014not because it\u2019s fashionable, but because it\u2019s the only scalable way to understand risk and regulatory change at modern market speed. Nzsdejan concludes, \u201cWe see a world where we can connect this ecosystem and reduce friction for all parties.\u201d<\/p>\n

Leveraging new technologies<\/strong><\/p>\n

Businesses are using AI, automation, NLP and advanced analytics to transform regulatory change management from a manual, reactive process into a proactive, data-driven process, claims Cathy Vasilev, co-founder and CCO at\u00a0Red Oak<\/a>.<\/p>\n

She said, \u201cThis seeks to develop real-time monitoring, predictive insights, automated impact analysis, and integrated workflows to obtain the data needed to fuel the compliance program. Of course, this requires technology in order to meet this need; manual processes are no longer sufficient. \u201c<\/p>\n

Is machine learning able to capture the nuance and intent behind evolving regulations? Vasilev believes that this is not the case, due to the fact RegTech is a deeply nuanced industry.<\/p>\n

She explained, \u201cAI cannot ingest and understand a rulebook with that nuance in mind. Regulators often use principle-based or intentionally vague language in rules. There are also important limitations when regulations hinge on context, ambiguity, or political\/legal interpretation. \u201c<\/p>\n

Vasilev added that ML does not understand political context, regulatory mood, or unwritten expectations, such as information in No action letters. ML can often misapply definitions across jurisdictions unless tightly constrained. \u201cIn other words, you need a human in the loop to help get the complete picture,\u201d said Vasilev.<\/p>\n

Meanwhile, Rick Grashel, co-founder and CTO at Red Oak, believes that the biggest barrier preventing organisations from taking regulatory text and turning it into actionable intelligence is a lack of complete context.<\/p>\n

He explained, \u201cRegulatory text is not enough to be able to take action. Details about each firm\u2014including the type of business, audiences serviced, jurisdictions serviced, types of products, size of firm, information systems involved, etc.\u2014must be considered alongside the regulatory text to begin to form actionable intelligence.\u00a0 And once actionable intelligence is developed, tactical plans of action have to be put into place in order to implement that intelligence.\u201d<\/p>\n

He added this is the \u2018entire purpose\u2019 of a firm\u2019s compliance organisation, which is to create a complete compliance program around the regulations, given the firm\u2019s specific details, risk appetite and needs.<\/p>\n

Keeping the human in the loop<\/strong><\/p>\n

According to Baran Ozkan, CEO of\u00a0Flagright<\/a>, firms are moving from static policy binders to live rule intelligence.<\/p>\n

He said, \u201cData teams stream official updates, use natural language processing to extract obligations, map them to a common control taxonomy, and push policy\u2011as\u2011code updates into monitoring and reporting systems. Dashboards show what changed, which controls are affected, and what evidence must be gathered next. Machine learning helps with the heavy lifting, such as clustering similar obligations across jurisdictions and suggesting control mappings, but it does not replace legal interpretation.\u201d<\/p>\n

For Ozkan, the safest pattern is retrieval-augmented generation with citations, explicit confidence thresholds, and mandatory human sign\u2011off before anything reaches production.<\/p>\n

\u201cThe biggest barriers are messy source formats, clashing definitions between regulators, and fragmented internal systems that cannot absorb changes quickly. Supervisors are starting to adopt similar technology for their own oversight, yet they will expect firms to prove provenance for every interpretation,\u201d said Ozkan.<\/p>\n

The Flagright CEO concluded by citing the company\u2019s view: it is vital to keep the human as the editor, keep every link from decision to source, and make policy changes deploy like software so an organisation is able to adapt in hours and not quarters.<\/p>\n

Read the daily RegTech news<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Regulations are no longer a fixed set of rules\u2014they\u2019re a constantly shifting landscape that challenges even the most seasoned compliance teams. Staying ahead isn\u2019t just about interpreting the rulebook; it\u2019s about seeing what\u2019s coming next. Data is proving to be the tool that brings clarity to this complexity, uncovering patterns and insights that were once […]<\/p>\n","protected":false},"author":4,"featured_media":11507,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"yoast_head":"\nHow data can help navigate a shifting rulebook - 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\/how-data-can-help-navigate-a-shifting-rulebook\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How data can help navigate a shifting rulebook - RegTech100\" \/>\n<meta property=\"og:description\" content=\"Regulations are no longer a fixed set of rules\u2014they\u2019re a constantly shifting landscape that challenges even the most seasoned compliance teams. 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