How Suade uses AI to make regulatory reporting faster and more reliable

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.