Date: Tuesday, 23 June, 2026
Duration: 45 minutes
Format: Live webinar

How are IReF, BIRD and AI‑ready data standards going to change the way banks think about regulatory reporting – not in theory, but in day‑to‑day delivery?

In this session, senior reporting and data leaders from Danske Bank, PwC and Suade will explore the shift from template‑based reports to granular, standardised data, and what that means for architectures, close processes and controls. We’ll discuss how initiatives like IReF and BIRD can reduce duplication and reporting burden, what “AI‑ready” really looks like for regulatory data, and the practical priorities banks should be tackling over the next few years.

Designed for Heads of Regulatory Reporting, Heads of Data and change leaders, this is a practitioner‑led conversation about building a reporting stack that can cope with rising supervisory expectations and the coming AI wave – without adding yet more complexity.

Register below.

LONDON, UK, May 21, 2026 – Suade Labs, a regulatory reporting platform used by financial institutions across global markets, today announced that its flagship event, RegTech Forum, supported by the City of London Corporation, will return to Guildhall in London on 6 October 2026. The in-person event will bring together senior regulatory reporting professionals to explore the future of reporting, supervision and regulatory change. 

The forum will welcome up to 400 attendees and feature five expert-led panel discussions, alongside smaller breakout sessions and targeted roundtables. This expanded format is designed to enable practical collaboration across the regulatory ecosystem, bringing together banks, regulators, policy specialists, compliance leaders, and technology and data experts navigating an increasingly complex reporting landscape. 

“RegTech Forum is special because it brings together people from different backgrounds, including industry, regulators and policy experts,” said Diana Paredes, CEO and Co-Founder of Suade. “This mix of perspectives allows for open and honest conversations about the issues that matter most. It is what makes the event so valuable and why it remains an important meeting point for the future of regulatory reporting.” 

The 2026 programme will cover key topics including AI in regulatory reporting, real-time reporting, Basel 3.1, SDDT, regulatory divergence and modern data architectures. Roundtables and breakout sessions will provide more focused, practical discussions tailored to different types of institutions. 

“Banking, regulation and technology all depend on human relationships,” said Neil Esho, former Secretary General of the Basel Committee on Banking Supervision. “You cannot build those relationships while sitting behind a computer screen. RegTech Forum creates an environment for the conversations that matter.” 

Taking place at a time of increasing data intensity and regulatory change, RegTech Forum 2026 offers a space for professionals to exchange insights, compare approaches and help shape the future of regulatory reporting. 

Event details 
RegTech Forum 2026 will take place on 6 October 2026 at Guildhall, City of London. For more information and registration, please visit our landing page. 

Regulatory reporting is entering a phase where three powerful trends are colliding: regulators are shifting to granular data models, supervisory expectations are becoming more continuous, and AI capabilities are moving from simple chatbots to agentic systems that can act across tools and datasets. For heads of regulatory reporting, data and risk, the question is increasingly not whether AI will play a role, but how to design data, governance and architecture so agentic AI improves control rather than undermines it.

1. Regulatory change has become a data problem

Across jurisdictions, supervisors are moving away from purely template-based collections and towards granular, common data models. That shift has three important consequences for reporting teams:

• One core dataset, multiple regulatory views. Firms are expected to maintain granular data that can support different supervisory perspectives, rather than building each report as a standalone artefact
• From periodic to continuous. As regulators gain access to richer datasets, the logic of fixed reporting cycles starts to erode. Oversight becomes more “always on”, and so must the associated controls
• Change manifests in data, not just forms. Regulatory change now often arrives as new attributes, revised definitions or different aggregation rules, rather than only as redesigned templates

In this world, regulatory change is fundamentally a data challenge. The institutions that cope best will be those that invest in a well-understood internal data model, with clear definitions and mappings to external standards, rather than those that focus purely on re‑engineering templates each time the rules move.

2. Clean, governed data is the precondition for AI

There is a well‑known maxim in AI: garbage in, garbage out. In regulatory reporting, that principle is unforgiving. Agentic AI systems can traverse datasets, fire off queries and orchestrate workflows at machine speed – but they can’t fix fundamental ambiguity in the underlying data.

For regulatory reporting, “clean data” means more than technically valid values:

• Reconciled across functions. Finance, risk and regulatory views must be consistent, with no “multiple versions of the truth” for core measures
• Fully traceable. Every number in a report should be explainable back to underlying transactions, with lineage that covers systems, transformations and controls
• Owned and governed. Data needs clear ownership, quality rules and a governance framework that treats it as a controlled asset, not a byproduct

AI can help here. It can draft or refresh documentation for complex calculations, highlight inconsistencies between definitions, and surface anomalies in large datasets far faster than manual methods. But those benefits only materialise when institutions have already done the groundwork on reconciliation, lineage and governance. Otherwise, AI simply accelerates the wrong answers.

3. From chatbots to agentic AI: what actually changes?

Most organisations have now experimented with conversational tools – chatbots that can answer questions, summarise documents or generate first drafts. Agentic AI goes further. Instead of waiting for one-off prompts, agentic systems can:

• Plan and decompose tasks into steps
• Call tools and APIs, not just handle text
• Interact with other agents to complete workflows

In regulatory reporting, this opens up use cases such as:

• Investigating why a control or validation failed
• Tracing the drivers of movement in a particular cell
• Mapping attributes between two regulatory data standards
• Drafting explanations or documentation for review

Crucially, none of this changes where accountability sits. As Murat Abur, Suade’s CIO, puts it: “AI, while it feels like it’s an intelligent being doing things – it doesn’t take away the accountability or the responsibility. It’s just a tool, like a hammer. The person using it is still going to be responsible.” AI can propose explanations, but humans still sign off on regulatory submissions and stand behind the numbers.

For that reason, agentic AI in regulatory reporting should initially focus on analysis, investigation and drafting – the work that supports judgment – rather than on making binding decisions.

4. What an AI‑native reporting architecture looks like

Agentic AI does not sit in isolation; it sits on top of architecture. For regulatory reporting, “AI‑native” architecture has a few defining characteristics:

API-first and granular

Agentic systems need the ability to ask precise questions of data. Instead of exporting entire datasets and hoping a model can cope, the platform must:

• Expose well-documented APIs that can fetch exactly the slice an agent needs (for example, “time deposits from this source system on this date”)
• Support filtering, aggregation and sampling in ways that make sense for reporting controls and for AI context limits
• Allow agents to iterate: ask a focused question, receive a subset, then refine further

If the only options are “all or nothing” – dumping millions of rows and hundreds of columns into a model – costs, latency and context limitations will quickly become blockers.

Open and interoperable

Regulatory data models will continue to evolve. Different supervisors will move at different speeds. To remain flexible, institutions need:

• A standardised internal model (ideally aligned with open standards) that can map to multiple regulatory schemas
• An ability to generate or transform data between models without brittle, one‑off builds
• Avoidance of black‑box systems where neither humans nor agents can inspect or query intermediate layers

Here, AI can act as an adapter: reading attribute definitions and sample data to propose mappings between schemas, or to generate test data aligned with a new standard. But the underlying design still needs to be open and documented.

Designed for interrogation, not opacity

As models become more capable, the difference between institutions that can interrogate their data at every layer and those that cannot will be stark. AI‑native reporting architectures are built on the assumption that:

• Every number, transformation and control may be questioned by humans, by AI agents, or by supervisors
• It must be possible to drill down, slice, and recombine data programmatically, not only through GUIs
• Closed, opaque systems will be increasingly unacceptable in a world of granular data, continuous oversight and model risk scrutiny

5. Practical agentic AI use cases – and clear boundaries

To move beyond theory, institutions need a pragmatic ladder of use cases, starting where risk is low and value is clear.

Low-risk, high-value use cases

• Documentation and knowledge management. Use AI to draft and refresh documentation for complex calculations, then have SMEs review and approve. Over time, this builds a high-quality knowledge base that both humans and agents can rely on
• Root-cause investigation. When a control fails or a number moves unexpectedly, agents can traverse lineage, query relevant slices of data and propose candidate explanations for human review
• Data quality triage. Agents can scan large batches for outliers, inconsistencies or missing values, prioritising issues for data owners to fix

In all of these, AI is a force multiplier for experts, not a replacement.

Medium-risk, controlled automation

Once comfortable with the basics, firms can move towards:

• Semi-automated workflows. Agents that, for example, investigate a set of validation failures overnight, group them into patterns, propose fixes, and route them to the right teams – with humans retaining the authority to apply or reject changes
• Schema mapping and regulatory change support. Agents that generate draft mappings between schemas or propose changes to accommodate new rules, again subject to expert review

Boundaries and what AI should not do (for now)

There are also clear lines that should not be crossed in the near term:

• AI should not have the final say on decisions that carry regulatory, legal or reputational consequences
• Agentic workflows should not be allowed to alter production reporting without traceable, human-approved controls
• Models must be monitored for drift, bias and unexpected behaviours; “set and forget” is not an option

The right test for each use case is simple: if the AI is wrong, can the institution detect and correct the error before it becomes a regulatory problem? If the answer is no, the use case is too far up the ladder.

6. A 3–5 year horizon: what leading firms will have in place

Looking a few years ahead, the leading regulatory reporting functions are likely to share several traits:

• Data-first operating models. A single, governed dataset that underpins multiple regulatory views, with clear ownership, lineage and BCBS 239 alignment
• AI embedded in everyday work. Documentation, investigations, testing and data quality are routinely supported by agents, reducing cycle times and freeing experts for higher‑order analysis
• Agentic workflows for change. Regulatory change programmes leverage agents to map schemas, generate test data, simulate impacts and surface implementation risks early
• Mature model governance for AI. AI tools are treated as models within existing frameworks, with defined controls for explainability, bias, drift and performance
• AI‑native platforms. API‑first, open architectures designed for fine-grained interrogation by both humans and agents, rather than monolithic, black-box stacks

For institutions thinking about their regulatory reporting roadmaps today, the most important point is timing. The models are improving quickly. Once they become very good, the gap between firms that have done the foundational work – on data, governance and architecture – and those that have not will widen rapidly.

Now is the time to:

• Consolidate and govern the regulatory data model.
• Bring AI explicitly into model risk and governance frameworks.
• Modernise reporting architecture to be open, API‑first and AI‑native.
• Build a small, well‑chosen portfolio of low‑risk agentic AI use cases and learn from them.

Suade’s platform was built as an API‑first, AI‑native regulatory reporting system, designed to align with these requirements and support “bring your own AI” operating models. If you are exploring how agentic AI fits into your regulatory reporting strategy, or what it would take to move towards AI‑ready data, governance and architecture, our team would be happy to talk.

Book a demo with a RegTech specialist today to see how Suade can help you prepare your regulatory reporting function for the next generation of AI‑driven workflows.

Save the date for Suade’s flagship forum in London on the Future of Regulatory Reporting.

On the 6 October 2026, 400 senior leaders from banking, regulation and technology will come together at Guildhall, City of London. This year’s forum will focus on what firms should prepare for next across AI, data standards, Basel 3.1, SDDT and cross-border complexity.

Key details:

Date: 6 October 2026
Location: Guildhall, City of London
Speakers: 25+ High-profile speakers
Attendees: 400+ Senior Industry Leaders
Audience: Senior Industry Leaders, Regulators, and Technology Experts

Previous speakers have included:

Phil Evans, Director Prudential Policy, Director Brexit, Bank of England, Lyndon Nelson, Deputy CEO, Bank of England, Neil Esho and William 'Bill' Coen, Former Secretary Generals, Basel Committee on Banking Supervision.

Register your interest today!

Speaker announcements and the full agenda will be shared soon.

Suade, a next-gen platform automating regulatory reporting for financial institutions through an API-first architecture, has set the standard for agentic AI as the only AI-native platform in the market.

London, 30 April 2026 — AI-native and cloud-native since 2014, Suade is an agentic AI ready regulatory reporting platform built on an API-first, open architecture. The platform has been built from the outset for automation, scalability, and programmable reporting.

Financial institutions are increasingly applying AI across data management, risk, and reporting. As this shift accelerates, demand is growing for systems that allow AI to operate in a controlled, transparent, and scalable way within regulated environments.

As the only regulatory reporting platform built natively for the agentic AI era, Suade enables AI to act as the interface to regulatory data and workflows while execution remains fully governed, controlled, and transparent.

Rather than being a traditional system with AI added on, Suade is designed so that AI becomes part of the operating model itself, integrated directly into how institutions interact with data, calculations, and reporting workflows.

Built on an API-first architecture and shaped by over a decade of automation-led design, Suade is built for programmable regulatory reporting and integration with modern AI systems.

Institutions can connect their own AI tools and internal systems directly to Suade using standard authentication, enabling flexible integration without vendor lock-in. All system activity, including external AI actions, is fully logged to provide a complete audit trail for governance and regulatory oversight. AI functionality is not enabled by default, ensuring institutions retain full control over its use.

“Most platforms in the market are legacy systems where AI is layered on as an afterthought,” said Diana Paredes, Chief Executive Officer of Suade. “That approach does not work in regulated environments where control, transparency, and structure matter.”

This creates a regulatory reporting environment that is programmable, transparent, and adaptable, aligned with how financial institutions are evolving their use of AI across the enterprise.

“We did not retrofit AI into Suade,” added Oliver Margetts, Chief Technology Officer of Suade. “We have been AI-native and cloud-native for over a decade. What is happening now simply validates the architecture we built from day one.” 

About Suade
Suade is a regulatory reporting platform used by financial institutions globally to automate reporting. Built on an API-first, open architecture, and AI-native and cloud-native since 2014, Suade enables institutions to manage data, calculations, and reporting processes within a controlled, scalable environment designed for modern finance.

Read the full PR here.

Date: Wednesday, April 22 2026
Duration: 45 minutes
Format: Live Webinar

The Basel III Endgame proposals mark a significant moment for US banks, with implications across capital planning, risk management, data, systems, governance, and business strategy. While the policy direction is becoming clearer, many institutions are now asking the same question: what should we do next?

This webinar provides a broad look at the implementation journey ahead, helping banks think through the areas most likely to require attention and coordination. Topics include early-stage planning considerations, the operational impact of the proposals, and how firms can begin shaping their internal response.

We’re joined by leading US regulatory experts, including Akhil Khunger, VP of Quantitative Analytics at Barclays, Fiona Humphrey, Executive Director of New Business at SMBC, and Kira Snow, Senior Implementation Expert at Suade.

Access the on-demand webinar below.