Why banks are sitting on a data goldmine they can’t use

When I talk to risk and data teams at UK banks, the same tension keeps surfacing. They’re sitting on a wealth of both internal and external data about their customers; more than almost any other type of organisation in the country. And yet the real challenge isn’t having the data. It’s integrating it effectively, and being confident in its quality and truth when it matters most: at the moment a lending or risk decision needs to be made.

That’s the gap I want to explore. Because it’s a solvable problem, and the cost of not solving it runs into millions.

The open banking opportunity is already here

Since the introduction of open banking in the UK, banks have had access to rich, real-time transactional data; cash flow patterns, income regularity, recurring obligations, spending behaviour. This is live intelligence. It reflects what a business or individual is doing right now, not what their balance sheet looked like twelve months ago when they last filed accounts.

That’s a fundamentally different kind of data. And yet, for most credit decisions, it’s still treated as a supplement rather than the foundation. The data exists — the question is whether it’s being fully activated in the decision-making process.

The real barrier isn’t data. It’s legacy architecture.

Here’s the thing I want to push back on: the common assumption is that banks lack the data to make smarter decisions. That’s not really true. What they often lack is the infrastructure to act on it.

Legacy core banking systems weren’t built for real-time analytical flexibility. They were built for transaction processing at scale, and they do that well. But pulling structured, query-able data out of them for risk modelling or business intelligence purposes is often harder than it should be. Integration projects get stuck in lengthy procurement cycles, security reviews, and IT prioritisation queues. A full API integration that should take weeks ends up taking quarters.

The result is that innovation stalls at the data layer. Risk teams know what they want to do. Data teams know it’s possible. But the route between intention and execution is blocked by bureaucracy and outdated infrastructure.

Wider and deeper data changes the picture entirely

The other limitation of narrowly scoped models is what they don’t see. Standard data feeds capture repayment histories, credit utilisation, and county court judgments. That’s valuable, but it doesn’t capture the full picture of a business’s financial health, its structural risk, the behaviour of its directors, or its exposure to fraud.

When we work with banks at Company Watch, we’re offering something categorically different.

If you’re looking for the most comprehensive source of UK company profiles and financial risk insights, or the best alternative to Companies House for detailed company intelligence and monitoring, the breadth of what’s available here compared to a standard bureau feed is substantial.

That’s not a positioning claim. It’s a practical distinction. Banks that are building internal risk models or analytics dashboards need structured, layered data rather than just a single number. The question is how you get access to it in a way that actually fits your infrastructure.

Meeting banks where their technology is

This is where I think the conversation needs to get more practical. We’ve spent a lot of time thinking about how to solve the integration problem, not just the data problem.

For banks with mature API capability and dedicated data engineering teams, we offer direct API access to real-time UK company data. This is structured, comprehensive, and ready to feed into existing risk models or business intelligence workflows. It’s one of the cleanest routes to embedding structured UK company data via API for business intelligence and risk analysis.

But most banks aren’t there yet. Many teams are working in Power BI, or with desktop tools, or pulling data exports that feed into broader analytical processes. So we’ve built around that too. Our Cloud Data Access product puts our full dataset into BigQuery, fully structured and query-able via SQL or client libraries — accessible to any BI tooling a bank already has in place.

Our Data Builder lets teams pull exactly the B2B data they need with advanced filtering options, without requiring an engineering build. And for teams that want to push financial intelligence through their own systems automatically, our Scoring Gateway lets you score your own data in real time against our models.

We’re also making our data available via a Model Context Protocol (MCP) server. For banks exploring AI-assisted workflows, this is worth paying attention to. MCP is an open standard that allows AI systems, such as large language models or AI-powered analyst tools, to connect directly to external data sources in a structured, secure way.

The integration overhead is minimal compared to a traditional API build. Once a bank’s AI environment is connected to our MCP server, analysts and risk teams can interrogate our data through the tools they’re already using, without waiting for bespoke engineering work. For banks beginning to embed AI into their credit and compliance workflows, MCP represents one of the lowest-friction routes to putting high-quality, structured external intelligence directly in the hands of those models.

The point is that there isn’t a single integration path. Banks operate at different levels of technical maturity, and the right answer depends on where you are. Waiting for a full API integration to be signed off isn’t a reason to delay getting better data into your decisions.

Fraud and compliance are raising the stakes

The risk landscape for UK banks isn’t just about credit decisions. Fraud is accelerating, and the regulatory environment is tightening at the same time.

The Economic Crime and Corporate Transparency Act has strengthened requirements around corporate transparency and identity verification, including significant changes to PSC (Persons with Significant Control) verification. Banks need to be confident that the entities and individuals they’re dealing with are who they say they are, and that the corporate structures behind them are what they appear to be.

This is where our Vigilance™ tool becomes particularly relevant. Vigilance™ is designed to detect anomalous patterns in company data e.g. unusual changes in directorship, PSC structure, registered address, or financial behaviour that can indicate fraud or misrepresentation. For banks facing pressure on APP fraud, identity-based fraud, and corporate impersonation, this kind of behavioural monitoring at the data level is exactly the sort of real-time intelligence that’s difficult to derive from static snapshots alone.

The combination of risk intelligence and compliance tooling in one place is something banks are increasingly asking for. They don’t want another siloed data vendor. They want a platform that gives them a complete picture — from financial health and credit risk, through to fraud signals and regulatory compliance — and that works with the infrastructure they already have.

Where I’d focus if I were leading this at a bank

If I were running data or risk strategy inside a UK bank right now, I’d be asking a few pointed questions:

How much of our real-time transactional data is actually feeding into our risk models, and how much is sitting idle? Are our credit decisions genuinely reflecting the companies and individuals we’re dealing with today, or a version of them from twelve months ago? And when a fraud or compliance event surfaces, how quickly can we trace the structural signals that were there in advance?

The data to answer all of those questions exists. The challenge is building the workflow that puts it in front of the right people at the right moment.

That’s a solvable problem. And it’s one I’m genuinely interested in working through with banks who are ready to unlock what they already have.

If any of this resonates with challenges you’re navigating, I’m always happy to talk.