Walk through any large organisation’s regulatory obligations register and you are likely to find the same cluster of problems: duplicate entries, outdated language, rules assigned to teams that no longer own the relevant functions, and gaps where a regulation was captured but never mapped to an actual business process.
According to AscentAI, these are not edge cases — they are the predictable consequence of building compliance infrastructure piecemeal, system by system, team by team, and acquisition by acquisition, with no unifying architecture to hold it together.
AscentAI recently delved into the topic of fragmented compliance data and why it is both inefficient and a risk.
The scale of the problem is well documented. In the 2026 AscentAI RegTech Benchmark Survey, 39% of respondents identified fragmented data and the absence of a single source of truth as among their top compliance challenges. For Tier 1 banks, that figure climbed to 67%.
When those same respondents were asked how artificial intelligence could support them, the need for unification came through in strikingly operational terms. Professionals cited needs such as creating a “single truth knowledge base,” eliminating “silos and department fragmentation,” consolidating reporting into “one system,” and achieving “full management of regulatory change through to controls modifications.”
The risks embedded in this fragmentation fall into three broad categories. The first is hidden obligation gaps. When regulatory obligations are scattered across multiple systems, spreadsheets, and teams, it becomes genuinely difficult to know whether the register is complete.
AscentAI reports that its onboarding obligation inventory examinations routinely surface both duplicate entries that introduce unnecessary complexity and genuine gaps where a regulation applies but has never been formally linked to the organisation.
The second risk is change propagation failure. When a regulation is updated, every policy, procedure, control, and piece of internal guidance that depends on it must also be updated. In a fragmented environment, that process relies on a chain of human communication that is inherently prone to delay, omission, and contradiction — making non-compliance not a question of intent, but of operational architecture.
The third is exam exposure. When a regulator or internal examiner asks an organisation to demonstrate compliance, the response must trace a clear, documented line from the regulatory requirement to the organisation’s actual controls. In a siloed environment, assembling that chain under pressure can be slow, stressful, and incomplete — precisely the kind of fire drill that senior compliance teams can least afford.
The alternative starts with what AscentAI calls an Obligations Inventory: a comprehensive, regulator-aligned register built from the actual text of regulatory documents rather than interpretations of them. Used as a foundation, this register can power automated change management across the compliance function — delivering real-time monitoring, automated obligation breakdowns, side-by-side comparisons of old and updated rules, policy and control impact notifications, audit trails, and automatic propagation through governance, risk, and compliance (GRC) systems via integration.
The strategic value of this architecture compounds at scale. As organisations expand into new jurisdictions, product lines, or markets, a unified regulatory data model scales with them, rather than forcing compliance teams to rebuild a siloed process from scratch each time. For organisations operating across multiple geographies, that distinction separates a compliance function capable of supporting growth from one that actively impedes it.
Read the full AscentAI post here.
Copyright © 2026 RegTech Analyst
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