Most AI projects in financial services stay in a pilot. Moneybox actually shipped one.
In this episode, we sit down with Marko Katavic, Director of AI and Decision Intelligence at Moneybox, to get the real story behind Aurora — the in-house AI guidance engine Moneybox built to help their 1.5 million customers better understand and act on their finances.
We go deep on what it actually takes to build an AI product inside a regulated environment: the architectural decisions, the trade-offs they made, the things that didn't work, and what the team is focused on next.
If you work in data, AI, or fintech — or you're trying to ship an AI product in a high-stakes environment — this is the episode for you.
We cover:
What Aurora is, what problem it solves, and why Moneybox built it in-house
The technical architecture behind a production AI product in a regulated context
How they approached FCA compliance, safety, and human oversight
The trade-offs they made — what they prioritised and what they deferred
What good looks like when you're measuring an AI financial assistant
What's next as the product and team continue to evolve
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