Why regulatory intelligence is now becoming vital infrastructure
Regulation was once positioned at the periphery of the financial system. A necessary constraint, reviewed periodically, handled reactively, and largely viewed as a cost of doing business. That perspective is starting to unravel.
Today, the sheer volume, speed and complexity of regulatory change mean that interpretation alone is no longer sufficient.
Firms are no longer just reading regulation; they are trying to embed it into their operations continuously, across jurisdictions and in near real time. In this context, regulatory intelligence is evolving from a support function into something far more integral.
At its core, regulatory intelligence is becoming the mechanism through which firms understand their obligations, an institutional “sensemaking layer” that converts fragmented regulatory signals into structured, actionable insight. Without it, organisations are effectively navigating in the dark, exposed not only to enforcement risk but to strategic errors driven by incomplete or outdated information.
For Luke DiRollo, CEO of Almis International, regulatory reporting sits in a strange position of the desk of every bank’s board and CFO. It is, he stresses, unquestionably critical to get right.
He said, “Failure carries supervisory action, capital penalties, reputational damage and in extreme cases, loss of licence. Yet at the same time, it is rarely viewed as a source of value. It is seen as a cost centre, a necessity, a commodity.”
DiRollo remarked that over the past decade, the cost of compliance has risen sharply, while the perceived benefit to the organisation has remained limited.
He added, “When regulatory reporting is discussed, the instinct is not to ask how it can improve decision-making, but how to ensure it is correct, complete and submitted on time. Accuracy is paramount, but insight is often secondary. The function becomes about reporting risk, rather than managing risk.”
This, DiRollo details, creates a paradox. The industry, he states, spends huge amounts of money on regulatory reporting yet much that spend produces little direct operational advantage for the companies that bear the cost.
“Reports are generated, validated, submitted and archived, often with minimal reuse of the underlying data. In many institutions, the reporting process exists independently from risk management rather than at its core. On its own, regulatory reporting adds little value. In many respects, the tail has been allowed to wag the dog,” stated DiRollo.
As the Almis CEO makes clear, this was not the intention. “The modern regulatory reporting framework was built in response to successive financial crises, with the aim of standardising how risk is measured across institutions and across jurisdictions. Regulators and policymakers needed a consistent way to assess the health of deposit-taking institutions within an interconnected financial system,” he said. DiRollo went on to detail that capital adequacy, liquidity coverage, leverage and interest rate risk in the banking book were not designed as internal management tools, but as supervisory lenses – a common language that would allow authorities to compare balance sheets, identify vulnerabilities and intervene before instability spread.
The result of this is a reporting architecture that provides a comprehensive, turn-key view of a bank’s condition from the outside.
DiRollo remarked, “Capital ratios, liquidity metrics, funding profiles and interest rate sensitivities together form a macro-level assessment of resilience. From a supervisory perspective, this framework has been remarkably effective. It has forced consistency, improved transparency and strengthened the overall stability of the system.”
The picture is very different from the bank’s operational perspective inside. The information required to produce these reports already exists within the institution – in finance ledgers, loan platforms, risk engines and treasury systems. However, it is rarely stored in a form that can be reused easily.
DiRollo said, “The information required to produce these reports already exists within the institution – in treasury systems, finance ledgers, risk engines, loan platforms, and deposit systems – but it is rarely stored in a form that can be reused easily. As a result, regulatory reporting often becomes an exercise in extraction, transformation and reconciliation rather than analysis. Teams spend more time aligning data than understanding it.”
This for him is why regulatory reporting has come to be seen as a burden rather than an asset. Not because the information is unimportant, but because the architecture used to produce it is fragmented, manual and disconnected from the way banks actually manage their balance sheets – and this is beginning to change.
DiRollo commented, “As regulation becomes more granular, more frequent and more data-driven, the traditional approach is becoming unsustainable. At the same time, regulators themselves are moving toward digital reporting frameworks, standardised data models and machine-readable rules. The direction of travel is clear: regulatory reporting is evolving from a periodic obligation into a continuous, data-driven discipline.”
This shift, the Almis CEO stresses, is forcing banks to rethink how they approach compliance. “The question is no longer how to produce each report, but how to build the infrastructure that makes all reporting possible. That infrastructure is what we mean by regulatory intelligence.”
He continued, “Regulatory intelligence is the ability to capture regulatory requirements in structured form, map them to a standardised data model and generate both regulatory and internal risk reporting from the same source. It is not a reporting tool. It is not a compliance workflow. It is the architecture that connects rules, data and risk management into a single system. At ALMIS International, we serve >60 domestic and international banks by providing a balance-sheet-native data fabric, underpinning risk, treasury, reporting and finance. One data-lift for the CFO’s entire ecosystem.”
The institutions what are treating regulatory reporting as a standalone function will see costs continue to rise, said DiRollo. Meanwhile, those that treat regulatory intelligence as core infrastructure will find that the same data used for compliance can also drive risk management, planning and strategic decision-making. Optimising returns sustainably.
He remarked, “At ALMIS, our view is that the turning point comes when banks stop trying to automate reports and start by commoditising the data that produces them. Once balance-sheet data is standardised, structured and reusable, regulatory reporting ceases to be a separate exercise. It becomes one output of a system that also powers internal risk reporting, liquidity management, interest rate risk, stress testing and management information.”
In that world, regulatory reporting is no longer just a cost of doing business. It becomes the foundation on which better banking decisions are made, said DiRollo. “That is why regulatory intelligence is rapidly becoming critical infrastructure,” he concluded.
Why manual regulatory tracking is unsustainable
Manual regulatory tracking has long been recognised as unsustainable, but the scale of the problem is now reaching a tipping point. Compliance teams are still expected to monitor vast numbers of regulatory bodies, websites and publications, manually scanning for changes across rules, laws and standards that apply to their organisation.
This approach is not only resource-intensive, it is inherently fragile and prone to human error, inconsistent interpretation and, critically, delays that leave firms working from outdated information.
4CRisk.ai COO and co-founder Supradeep Appikonda argues that the mismatch between regulatory velocity and manual capability has become too wide to bridge. “Tracking regulations manually has long been understood to be unsustainable,” he says, pointing to the growing burden placed on teams attempting to keep pace with constant change. The consequence is not just inefficiency, but risk — with missed updates or lagging responses carrying real regulatory and operational implications.
Technology is now fundamentally shifting that equation. AI-driven systems are capable of monitoring global regulatory developments at a speed and scale far beyond human capacity — often cited as 20 to 30 times faster — while maintaining a level of consistency that manual processes struggle to achieve.
These systems can ingest and process both structured and unstructured data, including dense regulatory texts and PDFs, extracting key changes, mapping obligations, and conducting applicability and impact analysis in near real time.
This shift is also addressing a more subtle but equally pressing challenge: alert fatigue. Compliance professionals, already under pressure to avoid missing critical updates, are often overwhelmed by the volume of information they must triage. By filtering, prioritising and contextualising regulatory changes, AI-powered tracking is not just accelerating workflows, but making them more usable.
In this context, the issue is no longer whether manual tracking is inefficient. That has been clear for some time. The real question is how quickly firms can transition to intelligence-led approaches before the gap between regulatory complexity and operational capability becomes unmanageable.
Manual regulatory tracking is also becoming unsustainable not simply because there is more regulation, but because the nature of regulatory change itself has evolved. The challenge is no longer confined to identifying updates, it is understanding how those updates cascade through the organisation.
Label CRO Scott Nice argues that many firms are still operating with outdated assumptions about what “tracking” entails. “Manual regulatory tracking is becoming unsustainable because the volume of change is now too high, and the impact of change runs much deeper into operations than many firms used to assume,” he says.
Historically, compliance teams could capture regulatory updates in spreadsheets, distil them into internal memos, and treat that as sufficient. That model is now breaking down. Regulation is no longer a static input to be recorded, it is a dynamic force that reshapes how firms operate at multiple levels.
Each regulatory change now demands translation into operational reality. Firms must determine how new requirements affect customer journeys, what additional data must be captured, how policies need to be rewritten, how workflows should be adjusted, and whether monitoring systems and reporting outputs remain fit for purpose. This is not a linear task, but a multi-layered process that touches nearly every part of the organisation.
The result is a growing disconnect between traditional manual tracking methods and the depth of analysis now required. What once functioned as an administrative exercise has become a complex, cross-functional transformation challenge. Without more sophisticated, integrated approaches, firms risk reducing regulatory change to surface-level awareness, while missing its deeper operational consequences.
Machine-readable regulation in practice
The industry is not yet at a point where regulation is consistently published in fully machine-readable formats, but in practice, that gap is already being bridged.
Appikonda suggests the question is less about waiting for regulators to change their publishing standards, and more about how technology is adapting in the meantime. “Already AI can deliver summaries, synopsis impact analysis, and intelligent obligation extraction, reading and parsing the multiple document types used by regulatory authorities and agencies,” he says.
In other words, while many regulators still issue guidance in fragmented, often unstructured formats, AI is increasingly capable of interpreting that material as if it were machine-readable. By ingesting and analysing these documents at scale, these systems can extract meaning, identify obligations and map changes with a level of speed and consistency that manual processes cannot match.
Crucially, this is moving beyond simple extraction. Modern systems are beginning to interpret intent, meaning understanding not just the “what” of a regulatory change, but the “why” behind it. That added layer of context allows firms to prioritise what matters, rather than being overwhelmed by volume.
This is particularly important in an environment defined by information overload. Compliance teams are no longer constrained by a lack of data, but by an excess of it. AI-driven regulatory intelligence addresses this by curating and tailoring alerts to an organisation’s specific regulatory footprint, filtering by industry, geography and risk profile, and automatically tagging and classifying changes based on relevance.
The result is a quiet but significant shift. Even without universal machine-readable regulation, firms are beginning to operate as though it already exists. powered not by regulatory reform, but by technology that can impose structure on complexity.
In practice, fully machine-readable regulation, in the sense of rules that can be directly consumed and executed by systems, remains some distance away. The complexity of regulation itself is a key constraint.
Nice points out that regulation is inherently layered, interpretive and context-dependent, making full standardisation difficult. “In practice, the market is still some way off from true machine-readable regulation in the way people sometimes talk about it,” he says. “There is still a big difference between structured regulatory content and regulation that can effectively execute itself.”
What is emerging instead is something more pragmatic: machine-assisted interpretation. Firms are increasingly equipped with tools that can identify regulatory changes, categorise obligations, map requirements to business processes and flag where policies or controls may need to adapt. This represents a meaningful step forward in operationalising regulation.
However, this should not be mistaken for full automation. The final layer, which is interpreting intent, assessing applicability and determining how to implement change, still relies on human judgement. Regulation does not operate in a vacuum; it interacts with business models, risk appetites and jurisdictional nuance, all of which require contextual understanding.
The distinction matters. While technology is materially improving how firms process and respond to regulatory change, it is augmenting human decision-making rather than replacing it. For the foreseeable future, regulatory intelligence will sit in this hybrid space, where machines structure and accelerate understanding, but humans remain responsible for interpretation and execution.
Will regulatory intelligence become a competitive advantage?
Regulatory intelligence is increasingly straddling two roles at once: a baseline requirement for operating in modern financial markets, and a source of competitive differentiation for those that can truly operationalise it.
Appikonda argues that firms adopting advanced regulatory intelligence capabilities are already seeing tangible benefits beyond compliance. “Those organisations that have adopted regulatory intelligence are able to provide stronger risk, resilience and compliance to their customers, partners and other stakeholders,” he says.
At a minimum, the direction of travel is clear. Rising regulatory complexity, increasing cyber threats and the realities of operating across multiple jurisdictions are making robust compliance and risk management capabilities non-negotiable. Regulators such as the U.S. Securities and Exchange Commission and the Financial Conduct Authority are also raising expectations, with demands for real-time or near real-time responses that traditional compliance models struggle to meet. In that sense, regulatory intelligence is fast becoming table stakes, which is a prerequisite for staying in the game.
But the more interesting shift lies in how leading firms are using it. Rather than simply reacting to regulatory change, they are beginning to anticipate it, identifying patterns, preparing earlier, and adapting faster than competitors. This has direct commercial implications, from reducing time-to-market for new products to building greater trust with clients and regulators alike.
As organisations invest in more integrated technology infrastructures, including compliance “control towers” that provide a centralised, real-time view of obligations and risk — they are also unlocking economies of scale. Regulatory intelligence, when embedded effectively, stops being a fragmented compliance activity and becomes a coordinated, strategic capability.
The result is a dual reality. For laggards, regulatory intelligence is an unavoidable cost of doing business. For leaders, it is becoming something more powerful: a lever for speed, resilience and competitive advantage in an environment defined by constant change.
Regulatory intelligence is following a familiar trajectory: it begins as a baseline requirement, but quickly becomes a point of separation between firms that can execute and those that cannot.
Nice frames it as a shift in where advantage is actually created. “At first, it becomes a cost of doing business, because every regulated firm now needs some level of regulatory intelligence,” he says. “But very quickly it becomes a competitive advantage for the firms that do it well.”
Crucially, that advantage is not about who identifies regulatory change first. In a world where information is increasingly accessible, the real gap emerges in execution — specifically, how effectively firms can translate regulatory change into operational change.
“The gap is not in who sees a rule change first,” Nice explains. “The gap is in who can turn that change into fast, clean operational change without creating disruption, rework or control weakness.”
This is where regulatory intelligence begins to shape performance at a structural level. Firms that can absorb change quickly, update processes in a controlled manner, and minimise customer disruption are simply better run. They spend less time on remediation, avoid costly cycles of rework, and reduce the friction associated with missing data or flawed downstream reporting. At the same time, they gain a clearer, more continuous view of where their risks actually sit.
As regulatory intelligence becomes more tightly integrated with core functions, onboarding, due diligence, workflow management and ongoing data monitoring, it ceases to be a reactive compliance activity. Instead, it becomes embedded in how the firm operates day to day.
At that point, the distinction is clear. Regulatory intelligence is no longer just about staying compliant, it is about building an organisation that can adapt, respond and execute with precision in an environment defined by constant change.
A model starting to break
For much of the past decade, regulatory intelligence was treated as a supporting function, important, but ultimately peripheral to how firms operated. Teams subscribed to updates, tracked key changes, leaned on external advisors where needed, and interpreted developments as they arose. It was imperfect, but broadly manageable.
Cardamon CEO Areg Nzsdejan argues that model is now reaching its limits. The issue is not simply an increase in regulation, but a structural shift in its complexity. “The challenge isn’t just that there is more regulation,” he explains. “It’s that the volume, overlap, and pace of change have increased to a point where manual tracking no longer scales.”
Firms are now navigating multiple regulators across multiple jurisdictions, each issuing rules, guidance, consultations and enforcement actions, often simultaneously, and often covering similar ground in subtly different ways. At the same time, organisations themselves have become more complex, offering a wider range of products across different markets, further compounding the challenge.
Critically, much of this regulatory output is not immediately actionable. It requires interpretation and context, tasks that still sit firmly with human experts. To operationalise even a single update, teams must identify it, read and interpret it, assess applicability, map it to existing controls, and determine whether gaps exist. That process is repeated hundreds of times each year, across entities and regulatory regimes.
At a certain scale, this ceases to be a question of efficiency and becomes one of feasibility.
What is now changing is not just the application of AI to summarise regulation, but the way firms structure regulatory information internally. Rather than treating regulation as static documents, leading organisations are breaking it down into discrete obligations, mapping those obligations at a granular level, and linking them directly to controls and risk frameworks.
This shift from unstructured text to structured obligations is what makes regulatory intelligence usable at scale. It enables firms to continuously answer questions that were previously manual and episodic: what applies to us, what has changed, what does it impact, and what action is required.
That is why regulatory intelligence is increasingly being treated as infrastructure rather than a support layer. It sits upstream of nearly every compliance decision; if that foundation is slow or fragmented, the effects cascade across the organisation.
In the near term, firms that get this right gain a clear advantage. They can enter new markets with greater confidence, launch products faster, and respond to regulatory change without the same operational strain. Over time, however, this capability is likely to become table stakes. Regulators are unlikely to accept “we didn’t see it” or “we didn’t have time to assess it” in an environment where regulatory information can be systematically processed.
The direction of travel is clear. Regulatory intelligence is shifting from something periodic and human-driven to something continuous and system-driven. The firms that recognise that early will operate – and compete – very differently from those that do not.
