Wednesday 9th September | From 17:30 | London, UK

The future of regulatory reporting is being shaped by regulatory change, AI, cloud technology and data transformation. For IT and delivery teams, this brings new challenges as well as opportunities to play a central role in major transformation projects.

Join us on 9 September for Byte and Slice, an evening for the teams building, running and improving regulatory reporting technology.

We’ll look at what these changes mean in practice for technical teams across banking, from architecture and data to systems integration and change delivery.

Come along for a practical discussion, pizza and drinks, with the chance to connect with others working across banking technology and regulatory change.

March 2026 – In our latest Suade webinar, implementation experts Luke and Priyank broke down the big EU reporting shifts coming down the track. Think CRR3/CRD6 finalisation, tougher RADAR checks, more detailed operational risk templates, and the ECB's IReF overhaul. It's a lot – but also a real chance to get ahead before 2029 deadlines hit. They shared practical ways to tackle data governance, automation, and linking everything together so compliance actually works for you.

CRR3/CRD6: Fluid Timelines, Rising Data Demands

Priyank kicked things off explaining how EU banks don't have the UK's clear Jan 2027 Basel 3.1 line in the sand – instead, CRR3/CRD6 standards are still being nailed down through 2026. The EBA wants to cut reporting costs 25% by simplifying things, but that's coming with way more granular data needs for operational risk, ESG, resolution reporting across FINREP, COREP, and SRB packs. Luke added that automation is key to fixing BCBS 239 lineage issues, with handy EBA tools helping smaller banks align risk, finance, and reporting.

RADAR/BCBS 239: Supervisors Turning Up the Heat

Priyank pointed out the ECB's 2026–28 priorities mean more intense RADAR scrutiny – think system-wide reviews and on-site inspections digging into old data governance gaps from SREP findings. Progress has been slow, so regulators are pushing harder. The upside? Solid RADAR setups let you leverage AI and analytics on clean data. His advice: automate fully, close those lingering gaps, and use EBA's mapping tools to get everyone on the same page.

Operational Risk: More Detail, Short Window
Luke walked through the EBA's updated COREP module in the 4.2 taxonomy – now with finer breakdowns like "other operating expenses." Good news: templates 1602–1604 got pushed to June 2026, giving a breather. But Priyank warned it'll still strain non-core data sources, ownership, and team alignment for better risk-sensitive capital views. Bottom line: use the deferral to test now and avoid last-minute scrambles.

IRIF: A Game-Changer for Stats Reporting
Luke Astri made IRIF sound like the future: the ECB's plan to merge BSI, MIR, SHS, and AnaCredit into one granular model, with a mid-2026 roadmap aiming for 2029 go-live. Backed by the Joint Bank Reporting Committee and BIR dictionary, it should ease long-term burdens with standardised data. Priyank flagged the catch – tight timelines mean banks need early chats with supervisors, JBRC guidance, and smart data plans to keep costs in check.

Turn 2026 Into Your Data Upgrade Year
Priyank framed 2026 perfectly: don't just patch – rebuild smarter before CRR3/CRD6 ramps up. Automate validation, reuse data across DORA, ESG, resolution, and payments. Luke Astri emphasised modular RegTech that handles EBA/SRB changes without constant recoding, giving boards and regulators confidence in your long-term plan.

How Suade helps EU banks with their Regulatory Reporting
Suade's platform adds EU rules on top of your existing setup for seamless parallel runs across borders and regimes. Get early updates to test RADAR, op risk, and IReF granularity, complete with lineage and reconciliations built in. It's how clients stay ready for CRR3/CRD6, IReF, and whatever's next.

Banks and financial institutions are under constant pressure to deliver accurate, timely regulatory reports while rules, taxonomies and data expectations keep shifting.

At Suade, we don’t see AI as a replacement for regulatory expertise. We see it as a multiplier that helps our teams design, implement and validate solutions faster for clients. 

 
Our approach: AI that supports experts, not replaces them 

Suade is built on deep experience in regulatory reporting, data models and supervisory expectations. That doesn’t go away because we use AI; it’s the starting point for everything we do. 

We use AI where it clearly helps our teams and our clients: 

• Automating repetitive, time-consuming implementation and validation work 
• Exploring complex rule logic and edge cases much faster than a purely manual approach 

We also lean on AI to turn subject-matter expert input into draft code and checks, instead of asking engineers to write every piece from scratch. That frees our specialists to focus on design decisions, edge cases and explaining outcomes to clients. 

Crucially, AI is never left to make regulatory calls on its own.

Every AI‑assisted output is reviewed, tested and refined by Suade specialists. AI helps with speed and coverage; people remain accountable for logic, methodology and sign-off.

 
Example 1: Solving SRB 2026 validation challenges with AI 

For the 2026 Resolution Reporting cycle, the Single Resolution Board (SRB) published a new taxonomy with additional data quality checks. These new validations are there to improve the consistency and reliability of resolution data across Europe. 

Within that taxonomy, our team encountered a very specific challenge. There was a group of validations that checked country equivalence via sub-regions.

That real‑world complexity made the logic hard to express in a simple, machine-executable way and also created exponential performance challenges.

Additionally, the team noticed an increasing number of cases where the documentation published by the regulator did not align with the formula that accompanied it. Finally, the deadlines for implementation were being measured in days, not even weeks. 

We had a choice: accept that these checks would sit outside the usual automation, or find a better way to model them and resolve inconsistencies in the documentation ourselves We chose the latter and used AI as part of how we got there. 

Here’s what that looked like in practice: 

• Our engineers used AI tools to explore different ways of representing the country–region relationships so they could be executed reliably in Suade. 
• AI helped enumerate combinations, surface edge cases and propose alternative structures for the checks. 
• The team then took those ideas, kept what made sense, and discarded what didn’t. The final logic was written, reviewed and tested by Suade engineers before going anywhere near production. 
• The team also used AI to correct the inconsistencies in the regulatory documentation. By simulating dummy data and implementations and cross-checking the XBRL taxonomies, the team was able to narrow their focus to certain parts of the taxonomy to resolve the contradictions in the
  documentation. 
• The team found that the regulatory documentation had, in most cases, updated formulas but not descriptions to be in line with the new taxonomies, which meant the error messages didn’t correspond to the data being checked. 

The end result is simple but powerful: several hundred validations were implemented in the platform in time for the deadline. That increases the coverage and robustness of our SRB 2026 Resolution Reporting checks and removes a chunk of potential manual work for clients. 

This is a good example of how we use AI more broadly. We’re not asking it to “invent” regulatory logic. We’re using it to help our experts think through complex problems faster and then turning that thinking into production‑ready validations. 

 
Example 2: Making Fireman transformations faster and smoother 

Fireman is Suade’s ETL framework. It takes client data from multiple systems and transforms it into Fire schemas, our standardised regulatory data model. Going from messy, inconsistent source data to clean, Fire‑compliant data is one of the hardest parts of any implementation. 

Traditionally, this work followed a familiar pattern: 

• SMEs define mappings between source systems and Fire schemas based on business understanding. 
• Engineers translate those mappings into transformation code. 
• The team iterates as they uncover edge cases and data quality issues. 

That approach works, but it can be slow and repetitive, especially when you are dealing with complex portfolios and multiple entities. 

Recently, our Head of RegTech, Albie Duffy, introduced an AI‑enabled way of working that speeds up the Fireman process without losing control: 

• SMEs still provide the mappings and rules. They remain the source of truth. 
• AI is used to convert those SME‑defined mappings into draft transformation code much more quickly than a person typing it all out. 

From there, Suade experts step in. They review the generated code, tighten it up, and make sure it’s correct, efficient and aligned with regulatory and data quality expectations. 

The impact is clear: 

• Turnaround time from SME input to working transformations is shorter. 
• Engineers spend less time on boilerplate and more time on tricky edge cases and performance. 
• Clients see working, production‑ready data pipelines sooner, which means faster time‑to‑value for new reporting projects. 

Once again, AI is doing what it does best: handling scale and repetition, so humans can focus on judgement and design. 

 
Governance: using AI safely, and in a way you can explain 

Because we operate in a highly regulated space, we treat AI as part of our control framework, not a shortcut around it. 

Internally, we’re clear about a few things: 

• Where AI can help (for example, scaffolding code, exploring patterns, suggesting test cases). 
• Where AI is not appropriate (regulatory interpretation, methodology decisions, final approval). 
• That anything AI touches still goes through version control, testing and peer review, just like manually written artefacts. 

We also document the final logic and approach, so it is explainable to clients, auditors and supervisors. If AI helped along the way, that doesn’t change your ability to understand and challenge the outcome. 

For clients, that means you get the speed and flexibility of AI-assisted development without losing transparency and control.

What this means for Suade clients 

Putting it all together, combining Suade’s regulatory expertise with carefully governed AI enables us to: 

• Implement complex new taxonomies and large validation sets more quickly, without sacrificing coverage. 
• Reduce the time it takes to onboard and transform new data sources into Fire schemas. 
• Let our specialists spend more time on analysis, design and assurance – the parts of the process that actually move risk and reporting outcomes in the right direction. 

AI will not make regulatory complexity disappear, but what it can do, when used in the right places, is help teams handle that complexity more efficiently and with fewer manual bottlenecks. 

That’s how we use it at Suade today, and it’s how we’ll continue to build AI into our platform and delivery model over time. 

The delay to the UK’s Basel 3.1 implementation date has given banks something rare in regulatory change: time. Instead of a compressed rush to the original go‑live, firms have an extended runway to recalibrate programmes, strengthen systems and resolve the uncertainties that surrounded earlier proposals.

With the final Basel 3.1 rules now in force and the 1 January 2027 start date confirmed, that time is no longer about waiting to see what the framework will look like. It is about making sure the framework can actually run – cleanly, repeatedly and explainably – across credit, market and operational risk, reporting and risk management.

Five months on from the final rules, the key question has shifted from “What do the Basel 3.1 rules say?” to “How ready are we to operate under them on day one?”

2. What has changed since the final rules

The near‑final Basel 3.1 package gave a strong indication of direction, but it also left gaps, drafting inconsistencies and areas where firms had to make assumptions. The final rules clarify much of this picture.

In particular, the final framework:

• Confirms the overall structure of the UK’s Basel 3.1 implementation, including key elements across credit risk, market risk, operational risk and disclosure.
• Introduces targeted refinements in areas such as market risk, where the details of the trading book framework and sensitivities‑based approaches matter for implementation.
• Provides technical corrections, clearer definitions and updated templates so that reporting and disclosure requirements align coherently with the policy intent.

For banks, this means most of the regulatory uncertainty is now resolved. The main variable is no longer the rules themselves, but the institution’s ability to embed them into data, systems, models and governance in a way that stands up to supervisory scrutiny.

3. Four pillars of Basel 3.1 go‑live readiness

To move from policy interpretation to operational readiness, it can be helpful to structure activity around four pillars: full compliance with the rules, reporting framework readiness, impact analysis validation, and risk management alignment.

3.1 Full compliance with the rules

This pillar is about making sure the core requirements across risk types are genuinely implemented rather than conceptually understood.

• Credit risk
  Banks need to ensure the revised standardised and internal ratings‑based approaches are fully reflected in their systems and processes. That includes updated risk weights, exposure classifications and the treatment of specialised lending. It also means testing how these changes flow through to capital, pricing and portfolio decisions.
• Market risk
  The move to the Fundamental Review of the Trading Book (FRTB) brings new sensitivities‑based calculations and stricter boundaries between trading and banking books. Firms must not only implement the formulae, but also have robust booking practices, clear desk structures and governance over model eligibility and approvals.
• Operational risk
  Transitioning to the standardised measurement approach (SMA) requires accurate calculation based on relevant business indicators and integration into capital planning. This is not just a change in calculation; it affects how firms understand, aggregate and manage operational losses and exposures.

Across all three risk types, documentation, testing and internal challenge are critical. “Implemented” should mean ready to withstand internal audit and supervisory review, not just coded in a single system.

3.2 Reporting framework readiness

Basel 3.1 is as much a reporting reform as a risk reform. New templates, data points and definitions will stretch existing infrastructure.

Key questions include:

• Are systems and data models updated to support the new reporting taxonomy and COREP templates required under Basel 3.1?
• Do revised Pillar 3 disclosures reflect updated definitions, assumptions and calculation logic, with traceability back to underlying data?
• Have firms run end‑to‑end dry runs of the new reporting packs, including data extraction, transformation, validation, sign‑off and submission?

Dry runs are particularly important. They help reveal data gaps, inconsistencies between risk and finance views, and process bottlenecks that may not be obvious from design documents alone.

3.3 Impact analysis validation

Most banks have performed some form of quantitative impact analysis during earlier phases of Basel 3.1 implementation. The final rules make it necessary to revisit those assessments.

Effective impact analysis at this stage should:

• Use the final rule set, current portfolios and up‑to‑date market data, rather than relying on older assumptions or draft proposals.
• Consider both point‑in‑time and forward‑looking impacts, especially where business models or balance sheets have evolved since the original analysis.
• Prepare and review the templates used for supervisory data collection, ensuring consistency with internal views and external submissions.
• Provide clear, board‑level narratives about capital implications, key drivers of change and the range of plausible outcomes under different scenarios.

The goal is to avoid surprises post go‑live and to ensure that management understands not just the numbers, but the drivers behind them.

3.4 Risk management changes alignment

Basel 3.1 is not just a technical recalibration of capital requirements; it has implications for how risk appetite, limits and risk processes are set and monitored.

Areas to consider include:

• Updating risk appetite statements and internal limits to reflect changes in capital requirements and risk-weighted assets.
• Aligning risk policies and procedures with new definitions, thresholds and methodologies, so that front‑line decisions are consistent with the regulatory framework.
• Revising ICAAP processes, risk and capital models, and stress testing to incorporate Basel 3.1 assumptions and outputs.
• Ensuring committees and governance forums have the information they need to oversee both the transition and the steady‑state regime.

Where multiple jurisdictions are involved, cross‑border coordination is also important to align local implementations with group‑wide policies.

4. Data, governance and explainability: the binding constraints

Beneath the four pillars lies a common set of challenges: data, governance and explainability.

Basel 3.1 introduces new data requirements across credit and market risk, including more granular risk sensitivities and enhanced treatment of unrated counterparties. It also extends disclosure expectations under Pillar 3. This places a premium on:

• Data completeness and accuracy across products, counterparties and risk factors.
• Clear data lineage from source systems through transformation layers to final reports.
• Defined ownership, controls and remediation processes for critical data elements.

Strong governance is needed to ensure that methodology changes, model updates and implementation decisions are well documented and subject to appropriate challenge. Training for finance, risk, IT and front‑line teams helps embed the new framework into day‑to‑day activities rather than treating it as a one‑off project.

Explainability is where these elements come together. Boards, senior management and supervisors will expect firms to not only produce numbers under Basel 3.1, but also to explain changes over time, reconcile to previous regimes and articulate the link between regulatory outcomes and business decisions.

5. Making the most of the remaining time

With the final rules established and the implementation date fixed, the remaining months are an opportunity to turn Basel 3.1 into a stable, repeatable process.

Practical steps for this phase include:

• Running a structured readiness assessment across the four pillars, identifying gaps between current and target state.
• Establishing a timetable for dry runs and parallel runs, including clear entry and exit criteria.
• Aligning technology and data initiatives with Basel 3.1 needs, avoiding short‑term workarounds that create long‑term complexity.
• Enhancing documentation, training and governance to support sustainable adoption.
• Engaging boards and senior management with concise, decision‑focused updates on progress and residual risks.

By treating the extended timeline as a chance to build durable capabilities rather than deferring effort, banks can enter 2027 with greater confidence in their capital framework and regulatory reporting – and be better positioned for whatever comes next.

Book a demo with a RegTech specialist

See how Suade helps financial institutions modernise regulatory reporting, improve control and manage change with transparent, data-driven technology.

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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 PwC, Banca d'Italia and Suade explored the shift from template‑based reports to granular, standardised data, and what that means for architectures, close processes and controls. We discussed 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 was 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.

You can access the full webinar below.

SDDT under Basel 3.1: considerations for implementation and reporting 

As the UK Basel 3.1 reforms approach implementation, firms are evaluating which prudential framework is appropriate for their size, complexity and business model. For eligible institutions, the Bank of England's Strong and Simple initiative introduces a simplified framework for Small Domestic Deposit Takers (SDDTs). 

While SDDT reduces complexity in several areas of the prudential framework, it does not eliminate the need for robust data, governance or regulatory reporting. Firms implementing SDDT should therefore consider not only the regulatory rules, but also the practical implications for systems, data and reporting processes. 

Understanding the SDDT framework 

The SDDT framework is designed for firms whose activities are relatively simple and predominantly domestic. Rather than applying the full Basel 3.1 framework in every area, eligible firms can benefit from simplified prudential requirements where the PRA considers that a less complex approach remains appropriate and proportionate. 

The objective is to reduce unnecessary regulatory burden without compromising financial resilience or supervisory oversight. The framework therefore simplifies aspects of the prudential rules while maintaining appropriate standards. 

Eligibility and ongoing assessment 

One of the first implementation considerations is determining whether a firm qualifies for the SDDT framework. This is not simply a one-off exercise at implementation—it requires ongoing assessment as the firm's business evolves. 

This involves: 

• Maintaining evidence supporting the firm's SDDT classification. 
• Assess how changes in business activities or risk profile could affect eligibility. 
• Review eligibility periodically as part of governance and regulatory reporting processes. 

This introduces an additional layer of governance, as firms must be able to demonstrate both initial qualification and ongoing compliance with eligibility requirements. 

Data considerations 

Although SDDT simplifies certain elements of the Basel framework, it still relies on accurate, consistent, and well-governed data. 

Firms should ensure: 

• Clear and consistent data definitions across systems 
• Reliable sourcing of required data points 
• Structured mapping between internal data and regulatory requirements 
• Appropriate controls over data quality and change management. 

Weaknesses in data quality or governance can lead to inaccuracies in reporting and increase the risk of regulatory challenge. 

Reporting remains critical 

One misconception is that a simplified prudential framework results in significantly simpler regulatory reporting. In practice, firms must still produce accurate regulatory returns supported by high-quality data and well-controlled processes. 

This includes: 

• Delivering reports within required timelines 
• Ensuring consistency across submissions 
• Maintaining documentation and lineage to support reported figures 

The ability to explain how results have been derived remains an important component of regulatory engagement. 

Common challenges in practice 

In implementing the SDDT regime, firms may encounter several practical challenges: 

• Interpreting eligibility requirements consistently across the organisation. 
• Updating systems to support new regulatory classifications. 
• Underestimating the data and reporting requirements associated with the framework 
• Ensuring data quality across multiple source systems. 
• Reducing reliance on manual calculations and spreadsheets. 
• Maintaining sufficient documentation for governance and audit purposes. 

Addressing these challenges requires a structured and well-governed approach. 

Supporting a robust implementation 

A sustainable approach to SDDT typically includes: 

• A clearly defined and documented eligibility assessment process 
• Strong data governance and standardisation 
• Transparent and consistent calculation methodologies 
• Automated and repeatable reporting workflows 
• End-to-end traceability for audit and regulatory review 

These elements support both initial implementation and ongoing compliance. 

Integration with broader frameworks 

SDDT should not be considered in isolation. It forms part of the wider Basel 3.1 landscape and must align with a firm’s overall regulatory reporting architecture. 

Firms should consider how SDDT interacts with other frameworks, reporting requirements, and internal systems. Approaches that prioritise consistency and scalability are more likely to support future regulatory change. 

A practical perspective 

While SDDT reduces complexity in specific areas, it introduces its own operational considerations. In particular, eligibility, data integrity, and reporting consistency remain key areas of focus. 

As with broader Basel 3.1 implementation, a structured approach that emphasises data quality, process automation, and auditability can support a more effective and sustainable outcome. 

Book a demo with a RegTech specialist

See how Suade helps financial institutions modernise regulatory reporting, improve control and manage change with transparent, data-driven technology.

Book a demo

Date: Tuesday, 23 June, 2026
Duration: 45 minutes
Time: 12:00 to 12:45 pm BST | 13.00 to 13.45 pm CET
Format: Live webinar

Register below.