Regnology is moving into the next phase of its expansion, applying agentic artificial intelligence to the regulatory reporting, risk and finance capabilities assembled through its recent acquisitions.
When RegTech Insight last spoke with chief executive Rob Mackay in early 2026, the company was integrating Wolters Kluwer’s Financial Risk and Reporting business and regulatory-reporting assets acquired from Moody’s. The focus was on bringing those products into a common architecture.One early measure of Regnology’s progress was its ability to absorb the acquisitions without disrupting clients. The Moody’s transaction presented the greater operational challenge because Regnology acquired regulatory reporting and risk management contracts and assets, rather than an established standalone business with its full supporting infrastructure.
“The team integrated both the WK and the Moody’s acquisitions really well, to the extent that we actually had Moody’s customers calling us,” says Mackay. “And bear in mind with Moody’s, we didn’t acquire a company. It was an asset deal where we acquired their Regulatory Reporting & ALM Solutions business. So, we had to stand up all of the processes, laptops, everything over a weekend. I got calls from some of the customers saying it was seamless.”
Regnology now brings global and local regulatory reporting together with risk analytics, finance processes and balance-sheet management. Mackay says Regnology is establishing a common architectural foundation across its reporting, risk and analytics domains. While a single data-ingestion model is not yet in place across all three areas, the long-term vision is to converge toward a more integrated data model as the solutions are brought together within Ascend.This matters to large institutions with reporting operations distributed across multiple businesses and jurisdictions. Regnology is working with a consulting firm and between 20 and 30 banks to assess the total cost of global regulatory reporting. Although the study and its methodology have yet to be published, Mackay says the annual cost for a sizeable bank can approach $1 billion. The scale of that expenditure helps explain the interest in using AI to reduce manual investigation and remediation.
Agents Take Action
Regnology introduced agentic orchestration with the Ascend platform at its November 2025 RegTech Convention. It is now selling three types of capabilities: workforce automation and analytical access: RGI Explain, the analytics companion that translates complex metrics, calculations and results into clear, plain-language explanations to explain data for users; RGI Assist, which goes beyond explanations to recommend specific actions, draft workflows, model scenarios and propose complex solutions; and RGI Workforce, the autonomous engine capable of executing complex end-to-end operational workflows.
Workforce automation is designed to move an exception from detection into remediation. Mackay illustrates this with a data-quality problem exposed during an overnight reporting run:
“You’ve seen dirty data show up in an overnight regulatory reporting system and it says, ‘my risk weighted assets just quadrupled.’ Well, I didn’t just go buy a load of stuff overnight, clearly, there’s a data issue.’”
Rather than leaving the reporting team to investigate the exception manually, Regnology’s agents can initiate the next stage of the workflow. “We can then kick off processes with these agents to then go clean that data upstream,” he says.
The example shows how Regnology is extending AI beyond querying or explaining reporting results. The agent uses an exception identified by the reporting system to start the process of tracing and correcting the underlying data problem, while retaining human oversight.
Regnology’s agentic portfolio gives users direct analytical access to the granular data held within its systems. The company has built Model Context Protocol (MCP) servers and agents that allow clients to interrogate reporting, finance and risk results.
“If you’ve done a balance sheet simulation, and you want to go talk directly to that cube of results, you can do that through the MCP server and our agents now,” Mackay says.
These capabilities serve different purposes. Analytical agents help users understand the data and results held within the platform. Workflow agents use reporting exceptions to set defined work in motion.
Granular Foundations
Regnology is connecting this agentic strategy to the regulatory shift towards granular data collection. The European Central Bank’s Integrated Reporting Framework (IReF) will consolidate statistical reporting requirements for euro-area banks. A pilot is scheduled to begin in the second quarter of 2030, followed by official reporting in 2031.
Mackay says Regnology is already signing its first IReF deals. He argues that a clean and reconciled representation of the balance sheet will give AI models a more dependable foundation than separate templates populated from fragmented sources. “Without a single granular data source that’s clean and reconciled with regulators, you risk applying your models to something that’s potentially unreliable,” he says.
Granular data could also make it easier to trace a material movement to the loans, deposits, securities or derivatives behind it. This creates the connection between regulatory simplification and agentic remediation: the more detail available to the system, the more precisely it can identify where an investigation should begin.
Wider Adoption
Regnology is also using agents within its own development processes, including what Mackay calls “hands-free bug fixing”, while retaining human involvement. He says banks and supervisors are approaching the company for help with their agentic transformation.
The proposed acquisition of Fed Reporter extends that strategy into the US community and regional banking market. Fed Reporter serves more than 4,000 institutions, giving Regnology a distribution network beyond the global banks targeted by its broader platform.
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