Managing regulatory data across 150+ systems

September 8, 2026 - 08:33

As financial firms wrestle with an ever-growing pile of rules, the quiet crisis is no longer about missing a deadline. It is about the messy plumbing underneath. With data scattered across more than 150 separate systems, many banks and insurers are finding that their biggest risk is not the regulation itself, but their own inability to see what they hold. Ahead of the QA Financial Forum in London, the conversation is shifting from simple compliance checklists to the harder problem of architecture.

The core issue is fragmentation. A single trade can live in a front-office system, a risk engine, a separate reporting warehouse, and a dozen spreadsheets in between. Each patch was built for a specific purpose, and none of them talk to each other cleanly. When a regulator asks for a view of exposure or a breakdown of collateral, the answer often takes weeks of manual stitching. That delay is no longer acceptable.

What is different this year is the focus on the data itself, not just the reporting output. Firms are starting to treat regulatory data as a product, with clear owners, quality rules, and a single dictionary of terms. The goal is to have one version of the truth that can feed any request, whether it comes from the PRA, the ECB, or an internal stress test. That means retiring old interfaces, building a proper data lake, and forcing vendors to open up their formats.

The speakers at the forum are expected to push on this point. Expect less talk about specific templates like COREP or Solvency II, and more about the underlying model. One session will look at how a large European bank cut its reporting effort by half after it mapped every data element back to a single source. Another will tackle the practical side of governance, asking who actually owns the data when it crosses three different business lines.

There is also a growing recognition that automation only works if the data is clean. Firms that rushed to add robotic process automation on top of broken feeds are now pulling those robots out. The robots just made the errors faster. The real fix is upstream, in how data is captured at the point of trade, how it is validated, and how it is stored for the long term.

For smaller firms, the challenge is even starker. They cannot afford a massive data engineering team, so they need simpler tools and clearer standards from the start. The forum will feature a panel on whether the industry can agree on a common data model, or if every firm is doomed to build its own patchwork again.

The mood is less panicked than it was five years ago, but more realistic. Nobody expects a single silver bullet. Instead, the winners will be those who treat data management as a permanent function, not a project. The sessions are designed to give practical steps, not just theory. If there is a takeaway, it is this: stop counting systems, and start counting the fields that matter. The rest is just noise.