Marc Gratacos, Founder and Managing Partner of data standardisation experts TradeHeader, explores how firms can move from crippling architectural stress to maximum efficiency by adopting standardised data models and supervised artificial intelligence.
First published in Finextra, 26 February 2026
The landscape of regulatory reporting is undergoing a fundamental shift. We have moved into an era where supervisors are becoming increasingly hands on, adopting a posture of invasive supervision that demands granular evidence and architectural clarity. For firms operating in the global financial markets, the traditional, manual approaches to compliance are now way out of date. The complexity of modern data requirements, coupled with the speed of global regulatory change, has created a state of constant architectural stress.
At TradeHeader, we see this stress manifest in the persistent issues that break post-trade reporting workflows. Whether it is the standardisation of data across disparate internal systems or the semantic identification of complex financial products, the hurdles remain significant. However, by embracing the FINOS Common Domain Model (CDM) and ISDA Digital Regulatory Reporting (DRR), and by strategically integrating artificial intelligence, firms can move beyond these hurdles towards a model of maximum efficiency, yielding reduced reporting errors, reduced risk and reduced costs.
The primary challenge in any reporting project is the effective standardisation of data. It is a task that sounds straightforward in principle but proves exceptionally difficult in practice. One of the first things a firm must address is how to organise information so it can be reported in a consistent and effective way. Firms often struggle to standardise events across a multitude of internal systems, each with its own legacy logic and data structures. When these systems fail to align, the entire reporting chain is compromised.
Product qualification and categorisation present additional friction. Correctly identifying a product type and ensuring it has the appropriate identifiers requires a deep understanding of semantics. Without this clarity, firms find themselves unable to meet the regulator's need for precise data.
Furthermore, we often observe that testing occurs too late in the project lifecycle. In a typical financial institution, data moves through multiple layers of architecture before it reaches the final reporting stage. If an error is discovered only during final validation, the cost of remediation can be immense. Teams must trace the break back through several layers of abstraction while working under the pressure of a looming regulatory deadline. This situation is exacerbated when a firm undergoes a system change or switches RegTech or data management vendors. Such transitions frequently reveal hidden layers of implementation that were previously undocumented or misunderstood, adding significant pressure to already stretched project teams.
The solution to these architectural stresses lies in the adoption of shared data standards and machine executable code, because the industry requires common data structures and validation rules. The Common Domain Model is the ideal tool for establishing these common structures. By using models like the CDM and DRR, the industry can move away from the ambiguous, inconsistent interpretation of text-based regulations and towards a shared, precise understanding of reporting requirements.
The benefits of this shift are quantifiable. Our own data and experience shows that there is a remarkably high level of overlap between different regulatory regimes. When a firm moves from reporting under EMIR to meeting requirements in the Asia Pacific region, for example, we see that between 80 per cent and 85 per cent of the code can be reused. If done carefully, this efficiency applies to both the data extraction logic and the validation rules.
But technology alone will not fix all ills. The success of the CDM and DRR depends on a robust governance framework. The industry must agree on the interpretation of the rules and the semantics of the data. When this data governance is in place, DRR becomes a powerful mechanism for cross jurisdictional compliance, allowing firms to scale their reporting capabilities without a linear increase in cost or complexity.
If we look at where this industry is going, the role of artificial intelligence and machine learning in regulatory reporting is becoming clearer. Those who have embraced AI and understand it well, know it is not a reliable replacement for human expertise, but it can excel as an essential partner for investigation and documentation. Crucially, a human expert must always be present to supervise the AI, verifying its outputs and ensuring that the logic remains sound.
Whilst Large Language Models (LLMs) operate as ‘black boxes’ with opaque ways of operating, the irony is that they can bring new levels of transparency to regulatory reporting, if used responsibly. Within DRR projects, we are already seeing the practical value of these tools. We at TradeHeader use AI to maintain high levels of code quality and to document the rationale behind the decisions made by industry working groups. Because DRR is built on the collective interpretation of experts, capturing the 'why' behind a specific piece of code is essential for long term transparency. AI helps us process the extensive documentation generated during these projects, suggesting annotations and recording the coding rationale in a way that was previously impossible.
Another significant application is the generation of synthetic test data. Testing is a critical component of any CDM or DRR implementation, but using real-world, often sensitive data carries significant compliance and security risks. By using AI to generate synthetic test data, we can validate systems and ensure they are fit for purpose without exposing the firm to unnecessary risk.
The industry is currently at a moment of transformation. The combination of data standards, machine learning, and smart contracts is fundamentally changing how reporting projects are developed and executed. We are moving away from a world of manual, retrospective compliance and towards a future of automated, real time reporting. For firms to thrive in this new environment, they must move beyond the mindset of fixing individual reporting gaps as they arise.
Instead, the focus must be on building a resilient, standardised architecture that can adapt to any regulatory regime. The goal for the coming years is to apply these new technologies strategically to create a compliance framework that is both efficient and future proof. The move toward maximum efficiency may be a technological challenge, but it is now a strategic necessity. By addressing the root causes of architectural stress and embracing the efficiencies of the CDM, DRR and AI, firms can transform regulatory reporting from a source of risk into a streamlined, high value operation.
As we look ahead to the next wave of regulatory reforms, the priority for every financial institution should be the standardisation of their data architecture.
By participating in industry initiatives like the CDM and DRR, and by staying at the forefront of AI integration, firms can ensure that the transition to the next generation of regulatory reporting is as efficient and effective as possible. The era of architectural stress is coming to an end; the era of standardised, automated efficiency has begun.
For those firms that have not yet adopted the CDM or DRR, their future adoption of these frameworks becomes more challenging the longer they wait. As co-creators and co-maintainers of these standards, we work with FINOS and ISDA to expand these models. More jurisdictions and functionality are being developed constantly and they are only going to expand from here.
It is like starting a marathon today that grows a mile longer if you start running it tomorrow.
The time to adopt CDM and DRR was yesterday, but today is still a very smart place to start.