Overcoming Regulatory Noise: How to Build Horizon Scanning for Actionable Intelligence (The Demo Room #25)
Welcome to The Demo Room – your front-row seat to the future of RegTech, RiskTech, and AI innovation.
In this series, we document our research interviews with the most forward-thinking vendors tackling the industry's biggest challenges. Each blog is built around a comprehensive product demo, providing clear insights into how these innovations address industry challenges.
On this occasion, we feature CUBE, a provider of AI-powered horizon scanning and regulatory intelligence for financial institutions and other regulated organisations.
Regulatory change is usually presented as a volume problem. Often, the harder problem is relevance.
A new publication may affect only one product, legal entity, jurisdiction or control. Another may repeat an existing position without changing the firm's obligations. A third may look minor at document level but alter a requirement that touches multiple policies and business processes.
The value of horizon scanning therefore depends on more than finding content. Firms need to know what changed, whether it applies to them, who owns the response and how the decision can be traced back to an authoritative source.
Generative AI makes it easier to summarise regulatory text. It also makes weak foundations more dangerous. A convincing answer based on incomplete coverage, stale context or an unverified source can move quickly into policies and controls. In our 2026 research, 57% of respondents rated the AI-related risk in compliance management as high or greater. Traceability and firm-specific relevance are becoming as important as the quality of the summary itself.
This is the problem CUBE is trying to solve: not only scanning the horizon, but connecting regulatory intelligence to action.
The Problem for Firms
Many change-management teams still receive document-level alerts. Specialists open a publication, work out whether it matters, compare it with prior text, identify the affected obligations and then distribute tasks across email, spreadsheets, GRC tools and business teams.
The process creates several forms of waste. Global institutions monitor overlapping issuing bodies in multiple languages. The same item may be assessed repeatedly by different teams. Broad keyword searches create noise, while unfamiliar terminology can hide relevant change. A useful decision may then lose its connection to the source as it moves into downstream remediation.
Generic AI assistants reduce reading time but do not automatically understand the institution. Applicability depends on context: a persistent view of its footprint, departments, products, services and regulatory priorities. Without that context, a model may produce a good summary of the wrong item.
The operating risk is equally important. If AI prioritises work, users need to see why. If a development is deprioritised, the evidence chain must still support challenge and review. Regulatory change cannot become a black-box filtering exercise.
A Solution: CUBE's Regulatory Intelligence Layer
CUBE combines a global regulatory data foundation, enriched with classification models and a regulatory ontology, with AI-powered coworkers that help teams work more effectively with regulatory intelligence. The platform is designed to take regulatory developments from capture through prioritisation and into the wider change process.
1. Capture and structure global content
CUBE captures publications from more than 10,000 issuing bodies across 250 countries and territories and 100 languages. Content is translated, classified and mapped into a common regulatory schema, with links to primary sources.
The company pairs this technology with more than 250 regulatory, risk, research and editorial specialists. The proprietary asset is therefore not only a generative interface, but a maintained and expert-validated regulatory data layer.
2. Prioritise against the firm’s unique context
A customer-specific "passport" records the organisation's footprint, departments, products and services. A background agent assesses new developments against this profile, alongside factors such as issuance type and certainty, and recommends which items deserve attention.
CUBE's regulatory-domain language models create concise overviews and key points. The user can see the reasoning behind prioritisation, inspect the source and amend the output rather than accepting an unexplained score.
3. Preserve the route from change to action
Teams can trigger impact assessments and workflow actions in RegFlow directly from a RegInsight item, linking the development to relevant obligations, controls and owners. Priorities Coworker creates a timestamped record of what was reviewed, when and against which obligations. Source links, discussion history and edits remain attached to the work, giving reviewers a traceable route from publication to decision and response.
Parker & Lawrence’s View
CUBE's strongest position is the regulatory data foundation beneath the AI. General-purpose models can summarise text, but they do not provide dependable global coverage, a maintained regulatory ontology or a persistent representation of a firm's regulated footprint by default.
The product also recognises that horizon scanning is not the end of regulatory change. Connecting prioritisation to obligations, controls and workflows gives the intelligence operational value and creates the evidence needed for review.
The continuous challenge for anyone in this space is to prove the quality of prioritisation. Buyers should ask about recall, precision, missed-change controls, multilingual consistency and the governance used when the customer passport is incomplete or out of date.
By applying customer-specific information, CUBE’s agentic coworker capabilities reduce the amount of “noise” in horizon scanning by ~60% compared to standard AI-powered solutions, based on early testing. CUBE will need to keep showing that the combination of specialist data, models and expert validation performs better than a generic assistant attached to a content feed.
As regulatory AI becomes commonplace, the interface will be easy to imitate. Trusted data, firm-specific context and an unbroken route from source to action will be harder to reproduce, and more important to compliance teams.
