On 1 December 2025, Regnology completed the acquisition of Wolters Kluwer’s Finance, Risk & Regulatory Reporting (FRR) unit; the deal was announced earlier in July. The company describes the combination as unifying its cloud-first regulatory reporting platform with FRR’s finance and risk capabilities, while extending its reach in key markets – APAC in particular. RegTech Insight sat down with CEO Rob Mackay and Chief Marketing Officer Mireille Adebiyi to better understand how the acquisition fits into the firm’s strategy.
Regnology specialises in regulatory and tax reporting and now serves around 10,000 banks and over 100 regulators globally, while FRR adds risk analytics and finance functionality that many shared customers already operate alongside Regnology’s reporting tools.Modernising Reporting Workflow
Regnology’s Reporting Hub (RRH) is the foundation of this approach. Mackay describes it as the firm’s flagship global regulatory reporting platform, designed to operate in the cloud and to support granular data consumption by regulators alongside traditional template-based submissions.
Launched in November 2025, Regnology’s Ascend initiative builds on the RRH foundation by introducing workflow and operational controls into the reporting process itself. “It adds a whole suite of workflow tools into a traditional regulatory reporting platform,” Mackay explains, including workflow overviews that show where processes have failed, alongside automation intended to reduce manual effort.
The rationale is rooted in how reporting interfaces have historically been designed. “The way that both regulators and banks view their data is essentially electronic paper,” he says. “It’s a template which has a bunch of calculated fields in it, and where there is granular reporting, it’s a very long electronic piece of paper with a list of instruments and fields, and that’s a poor way for human beings to consume data.”Ascend introduces exception monitors and key risk indicator dashboards – tools more commonly associated with Straight Through Processing (STP) in trading.
Straight Through Reporting (STR)
Adebiyi positions Ascend within Regnology’s broader Straight Through Reporting (STR) vision, describing it as a shift away from high-touch, manual regulatory processes toward automation embedded across the reporting lifecycle. As she puts it, the focus is on “making this a streamlined process rather than high-touch with manual retreatment.”
Mackay gives a practical example of why this matters. In many institutions, data quality checks are applied only after a regulatory report has been produced. Errors are discovered late, often by finance teams reviewing submissions. “That’s a really bad place to catch these data errors,” he says.
Regnology’s approach is to push controls upstream. “Rather than banks fixing data errors with manual workarounds in the regulatory reporting process, we actually automate the process of fixing data in upstream systems.” The intention is to reduce remediation at the reporting stage by addressing data issues at their source.
Cloud Pre-Requisite
Regnology’s strategy is underpinned by the fact that cloud infrastructure is mandatory for the market to move towards full balance-sheet granular data collection. Mackay links this directly to data volumes. As supervisors shift from aggregated templates to record-level data, on-premise infrastructure becomes a constraint.
Mackay recalls the state of the market when he entered the business as fragmented, with widespread customisation and multiple client-specific versions. “Every client was using a different version with customizations,” he recalls, making platforms expensive to maintain and difficult to evolve.
Regnology’s response was to impose standardisation: phasing clients onto a common version and migrating them to the cloud. Since 2023, Regnology has invested over $100 million in rebuilding its core infrastructure to be cloud native. “We standardized that, we got them all onto the same version and onto the cloud,” Mackay says. As a result, over 80% of Regnology’s clients now operate on cloud infrastructure, and that same approach is being continued with the FRR customer base.
Mackay points to a recent implementation in Poland as evidence of what standardisation and cloud delivery enable in practice. Referring to BPS, “the largest banking group in Poland… a cooperative of 307 different banks,” he explains that Regnology was able to execute the migration at pace – “They signed with us… about mid-December. And we took them live for their reporting just four weeks later. And that was over Christmas.”
Adebiyi adds that the programme formed part of a broader digitisation initiative and DORA alignment, describing it as “a huge first milestone” that demonstrated how Regnology’s cloud-native, standardised platform can support transformation at scale.
How FRR Extends the Model
Regnology already operated a single ingestion model designed to capture a granular representation of a bank’s entire balance sheet, across deposits, lending, securities, investments, and derivatives. Once that data exists, Mackay argues, it naturally supports more than regulatory reporting. Risk analysis, balance-sheet simulation, and asset-liability management can all be driven from the same dataset. Integrating FRR’s risk and finance capabilities into this environment extends Regnology’s existing architecture. The result is an integrated environment in which regulatory reporting and risk analysis are driven from the same governed data, reducing duplication and operational cost for firms.
Throughout the discussion, Mackay returns to the balance sheet as the organising principle. Capital calculations, liquidity analysis, expected credit loss, impairments, and asset-liability management all depend on granular balance-sheet data. Many of these calculations also feed back into regulatory reports.
In this context, separating regulatory reporting from risk and finance is operationally inefficient. Regnology’s strategy is to treat regulatory reporting as one of several downstream uses of a shared dataset rather than a standalone process.
Another pillar of Regnology’s proposition is regulatory change management. Mackay describes this as a core capability, supported by a large, dedicated organisation.
“With the WK acquisition, Regnology has a team of approximately 330 people dedicated just to horizon scanning and staying on top of regulatory change,” he says. Their role is to maintain templates, monitor validation rules, and perform regulatory interpretations across jurisdictions and to push updates automatically to clients.
The objective is to mutualise a cost that institutions would otherwise bear individually. Clients do not need to interpret regulatory change or implement updates themselves; those changes are incorporated into the platform as part of the service.
Moving to Granular Data
From Regnology’s vantage point – serving both regulators and the regulated – the move towards granular data collection is described as a global trend. Mackay cites examples including granular credit reporting in Europe, similar approaches across parts of Asia, and ongoing implementations in Canada.
Regnology’s vantage point across both banks and supervisors is reinforced through its annual RegTech convention, which Mackay describes as a forum bringing together “about 2,400 professionals from the regulatory reporting and supervisory world.” The most recent event included “representation by more than 120 different regulators,” reflecting the scale of supervisory engagement. Adebiyi notes that discussions at the event signal a broader shift, with regulators increasingly open to cloud and AI initiatives and a shared recognition that modernisation is becoming unavoidable.
The longer-term destination described by both executives is a shift from a push model – where banks generate reports in response to regulatory requests – to a pull model where regulators can query governed datasets directly.
Mackay points to event-driven data collection regimes already in place in some jurisdictions as evidence that this is more than theory. In such models, data flows are automated and continuous, reducing reliance on periodic submissions and manual intervention.
Balance Sheet Centricity
For Mackay, balance sheet management is foundational to regulatory reporting. Regnology’s architecture is built around what he describes as “a granular single ingestion model for a granular representation of the complete balance sheet.” From that dataset, balance sheet management functions, including capital calculations, liquidity analysis, asset-liability management, and regulatory submissions, all derive from a single auditable source. As supervisory regimes evolve toward fuller balance-sheet data collection, Regnology is positioning itself around a single premise: that regulatory reporting, risk, and finance are most effective when driven from the same governed, cloud-native data foundation.
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