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DATA & TECHNOLOGY

A Practical Data Maturity Roadmap for Financial Crime Teams

Matthew Beattie
Author

Matthew Beattie

Most banks are sitting on a goldmine of data – but instead of treasuring it, they let it pile up like junk in a scrapyard. 

This is especially true in financial crime. Every KYC file, sanctions hit, transaction alert and customer risk score is a valuable clue, pointing to where threats are growing or opportunities draining away. Yet rather than piecing these clues together, most banks let them wither away in silos, too often ignored. 

If your CEO or the regulator asked you tomorrow to show exactly where your financial crime risks sit – which records they involve, where in the process they appear, and to quantify how your controls are mitigating them – how confident would you be? 

The answers are in your data. But to see them clearly and easily, you’ll probably need to reach a higher level of what we call ‘data maturity’. That’s what turns scattered clues into a complete picture, gives boards and regulators confidence, and ultimately helps you serve customers more efficiently and profitably. 

 

Data
Data

Building true data maturity is a journey that steadily strengthens your people, processes, technology and governance along the way. 

At BeyondFS, we’ve guided organisations through this journey from start to finish. We’ve seen where it goes wrong, and what makes it work. Here’s what that path looks like – and how you can climb the data maturity ladder step by step. 

The Climb: How it happens

It almost always starts the same way: fragmented data, no shared truth, and little trust. Analysts work in silos, firefighting with spreadsheets, while definitions vary wildly and governance is absent. Processes rely on individual judgement, and queries from regulators or auditors often spark panic. 

The first step up is data discovery. Tactical dashboards or prototypes start to show value, usually thanks to a few individuals who champion better use of data. Some key paths get documented, and small wins begin to replace gut feel. It’s patchy, but it proves there’s something to build on. 

Next comes cross-team collaboration. Shared data pipelines and centres of excellence start to appear. Manual tasks like case closures are automated with scripts, freeing up time and improving consistency. A more stable MI platform emerges, and governance tightens through thresholds and monitoring. Teams restructure into cross-functional groups, working on shared problems rather than fighting over whose spreadsheet is right. 

Stage four is the big cultural shift: an enterprise taxonomy. People talk about data in the same way, use common tools and techniques, creating a baseline of literacy across the bank. APIs link systems, audit trails support compliance, and standardised process maps keep decisions increasingly system-driven. Governance becomes intuitive, defining how data is structured and managed across the organisation. 

Finally, the bank becomes a data-driven institution. Data is audited, fully embedded in decisions, and flows without undue consideration from source systems to executive decision packs. Processes adjust themselves in near real time, governance rules are enforced in code, and data-led thinking becomes instinctive. People naturally look to data before acting – it’s simply how business gets done. 

Fig 1: The Data Maturity Journey

BeyondFS Data Maturity Model (1200 x 1200 px)

Download this table as a PDF here.

The pay-off as you climb

This does, of course, involve hard work and time. But taken step by step it doesn’t feel like that big a change. It’s worth it. Each step up the data-maturity ladder brings real, measurable rewards. 

At the most basic level, simply being able to count your clients properly makes regulator visits far less fraught. You’re no longer scrambling to explain conflicting numbers or pulling together last-minute reconciliations. It stops budgets spiralling out of control, because you finally know the true scale of your obligations. 

As data matures further, the benefits accelerate. By ironing out lumpy scheduling, some banks have cut up to 30% off contractor bills – reorganising workloads to smooth peaks that previously entailed expensive short-term hires. With predictive staffing, you can start funding permanent hires based on proven savings, rather than signing off emergency overtime to avoid missed deadlines. 

In another example, a sales team had tried to pre-empt an inefficient onboarding process by pushing new clients into it long before they were ready. This, ironically, drove up volumes and made everything even slower, as the onboarding team were now wasting time on cases that often dropped out. 

By introducing a simple shared dashboard showing exactly where cases were and where the holdups lay, both sales and onboarding got the visibility they needed to cut volumes, speed things up, and make the whole operation far more efficient.  

How to steer clear of the traps

There are well-worn pitfalls that trip up many organisations on this journey. The biggest is holding out for 'perfect data' before doing anything. We advise firms not to wait, but clean as they go, letting each solved use-case fund and justify the next. 

Another common misstep is buying shiny new tools without a clear question to answer. We’ve seen banks invest heavily in new technologies that spend months or years gathering dust because no one tied them to an immediate problem. 

Relying on individual heroes is risky too. If only one analyst understands the pipeline, you’re exposed. Document, automate and cross-train so the operation is resilient if someone moves on. And governance can’t live in a PDF on a shared drive - it needs to be embedded in decision logic so rules are enforced automatically, not left for busy teams to remember. 

Keeping it practical and making it stick

The key is to start with an honest look at where you are today. Map each domain against the maturity model, then pick a real problem to solve – one that hurts. For example, tackling that September spike in KYC reviews. Place data people shoulder to shoulder with business or operational owners until it’s fixed. 

Automate data feeds, build simple dashboards, and make performance transparent. Shared, accurate metrics will show where SLAs are missed and allow you to make targeted changes. 

Most importantly, aim for the next rung, not the finish line. Reinventing everything at once won’t work. By reinvesting each saving into the next round of improvements, the programme effectively funds itself. Over time, what began as a way to tidy up fragmented data becomes a core engine that runs the business more efficiently and decisively. 

Ready to move forward?

Whether you’re still drowning in spreadsheets or already working on a common taxonomy, every financial crime function can climb towards a data-driven model, step by step.  

We’ve helped clients uncover six-figure efficiencies within a single quarter simply by focusing on the right next move. If you’d like a no-strings walkthrough of your own data maturity opportunities, we’d be glad to help. 

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