My journey to Wise: from legacy banking to fintech

I joined Wise six months ago after 16 years in traditional banking. A few former colleagues had transitioned here and highly recommended the culture, and the shift from legacy systems to fintech felt like the perfect next challenge.

Today, I lead the global Quality function for Customer Support and Payment Operations, managing teams across Tallinn, Budapest, Singapore, and Tokyo. My remit covers four pillars: Quality for Customer Support, Quality for Payment Operations, Root Cause Analysis & Investigations, and Quality Automation.

Our team sits right between Operations and Engineering. Every quality gap we find—whether a flawed process, a product friction point, or a weak control—is a signal. My team handles the root-cause analysis, translating these gaps into actionable insights for product and engineering teams so we can fix them permanently. Bridging that gap is where we drive real impact, ensuring our execution matches the Wise mission.

Redefining quality through automation, AI, and data

The traditional quality model—analysts manually reviewing a tiny sample of cases against a checklist—doesn't scale. It’s retrospective and subjective; by the time you find a friction point, the customer has already felt it.

We’re rebuilding that model by moving from manual sampling to full-coverage, data-driven assessment. We build pipelines that consolidate signals from support, payments, and investigations into a single view, then apply pattern mining and AI to systematically surface the highest-impact friction points.

Instead of asking analysts to find needles in haystacks, the data surfaces the issues that matter most so our specialists can focus entirely on root-cause resolution. We are shifting the function from subjective grading to consistent, explainable outcomes, and moving from simply asking "what went wrong?" to fixing it at the source.

How are you reimagining the role of Quality?

We are shifting Quality from a downstream check that flags past mistakes into an upstream signal that shapes how products are designed, payments are routed, and customers are served. When you provide trustworthy, evidence-backed data, teams stop debating whether an issue is real and start focusing on how to fix it. Investigation times drop, and remediation gets faster.

There's a Wise saying I love: "the best support is no support." I’d add that the best quality check is the one that never has to happen because we've already designed the problem out of existence.

What do you look forward to in your day to day role?

Honestly, it's the mix.

In a single day, I might be sketching out how we design a new automation model or improve the quality score of an existing one, untangling dependencies to keep a key delivery moving, brainstorming sharper approaches to our payment quality checks, or working with partners across Servicing to drive AI adoption and implementation. It keeps me sharp, and it keeps me curious.

But the moment I love most is when a piece of work we've done actually starts driving change downstream, generating insights that improve a product, unpacking a customer issue we hadn't fully understood, or shaping a process for the better. That's when the work feels real.

And beyond the day-to-day, there's a bigger ambition pulling everything together: we're building a function that combines human expertise, data, and AI in a way that hasn't really been done before at Wise. Helping shape that is what makes every day interesting.

How do you foster seamless collaboration across these different time zones or teams?

It comes down to clarity and trust. With a team split across four time zones, you can't rely on hallway conversations. We avoid ambiguity by writing things down, defaulting to async, and protecting live meetings for discussions that actually need them.

You also cannot lead a high-scale global team by trying to be in every room. I focus on building regional leaders who are fully empowered to make critical calls without waiting for London to wake up. Wise’s culture of ownership makes this possible. Trust is our baseline, and that is what keeps us fast.

When you are hiring, what are the non-negotiable traits or skills you look for that don’t necessarily show up on a CV?

A CV tells me where someone has been, but it rarely tells me how they think. For my team, three traits matter most:

• Structured thinking: When faced with an ambiguous problem, can you break it down logically? I look for people who ask the right questions and prioritise effectively, rather than rushing to a premature solution.

• Problem-to-solution execution: Plenty of people can spot a flaw, but few can take a vague issue and map it cleanly from symptom to root cause to ownership. That ability to drive a fix is the biggest predictor of impact on our team.

• An automation mindset: I look for people who don't wait to be upskilled. They track where data and AI are going, test ideas on their own, and instinctively look at manual processes to ask how they can be built differently.

Ultimately, it comes down to one thing: the ability to take complexity and give it clarity. If you can do that, the rest is teachable.

And what would be your advice for those looking to join Wise? 

Bring your ideas, and we'll give you the speed and the tools to make them real.