Building smarter finance: Lessons on leading with AI

AI is no longer just an emerging tool, it’s becoming core business infrastructure. We’ve embraced AI as a key driver of our mission to make car finance faster, fairer, and more flexible. But leading with AI isn’t about chasing hype or deploying tech for its own sake. It’s about thoughtful, responsible integration that delivers real value to customers.

Here’s how we’re exploring the use of AI to reshape operations, scale responsibly, and stay ahead in a sector that’s evolving fast - and what we’ve learned along the way.

Building AI into the core 👷

We use AI across customer service, marketing, and increasingly in core areas like credit decisioning, risk modelling, and fraud detection. The goal? Real-time lending decisions that are both responsible and scalable. It’s not just about automation, it’s about removing friction from the customer journey without compromising on risk controls or regulatory compliance.

As a digital-first lender, we have to - and can - scale fast. AI helps us do that while keeping the customer experience front and centre.

Lessons from the frontline 🧑‍🏫

One of the biggest early lessons we learned was this: AI solutions need domain expertise from day one. Technical metrics alone won’t deliver real-world impact. That’s why every AI initiative at Carmoola starts with clearly defined business goals and deep collaboration between our data science, product, and compliance teams.

That upfront alignment means we move faster, and deliver outcomes that actually matter.

Collaboration > silos 🤝

AI has made us more cross-functional. Our data scientists work hand-in-hand with engineers, marketers, risk analysts, and operations teams. That ensures the models we build are technically sound, operationally viable, and aligned with customer needs from the very beginning.

It’s also raised the bar for data literacy across the business, helping every team feel more confident in using and understanding AI tools.

Knowing what (and what not) to automate ⚖️

We assess potential AI applications by asking three key questions:

1. Is the process repetitive with clear data inputs?
   
   
2. Is there measurable business impact?
   
   
3. Do we have enough high-quality data to train a model?

But there’s a fourth, equally important question: do we have human expertise to oversee and guide the system? In highly regulated or nuanced areas, we often choose augmentation over full automation. "Human-in-the-loop" design is a big part of how we operate, and we’ve developed clear principles and ethical guardrails to ensure the best outcomes for users, the business, and for customers.

Balancing speed with responsibility 🏎️

We believe in rapid iteration, not reckless rollout. Every model that could impact customer outcomes is subject to governance checks, explainability standards, and ongoing human review.

Bias, drift, and overdependence on automation are real risks, and we manage them by designing processes that are transparent and safe to evolve. Speed doesn’t mean skipping steps. It means building systems that learn and improve without putting customers at risk.

When AI falls short 😖

Not every AI experiment works out. We trialled a natural language processing model to auto-categorise support queries, but in our high-sensitivity environment it didn’t deliver the accuracy we needed.

Now we use AI to assist, not replace: it routes basic queries and flags urgent ones for a human, combining efficiency with empathy. That blend works better for our customers and our team.

No legacy? No excuses 👶

Being a startup gives us an edge: we’ve built Carmoola with AI in mind from day one. But that doesn’t mean we’re immune to the cultural challenges that come with emerging tech. We invest in internal education and transparency to ensure every team trusts how AI fits into their workflows.

It’s not about replacing humans, it’s about empowering them.

What will separate AI leaders from laggards? 🏃‍➡️

Three words: data, iteration, transparency. The fintechs that succeed won’t just build fast, they’ll build on solid ground. Clean, representative, well-governed data is the unsung hero of any AI success story.

Ethical transparency also matters. Customers deserve to know how decisions are made. The companies that get this right will earn trust, and the right to lead.

Final thoughts: AI as co-driver 🤖

We don’t see AI as a replacement for human decision-making. We see it as a force multiplier. One that makes us faster, sharper, and better able to serve our customers.

The startups that thrive in this space won’t just be the most technical. They’ll be the most thoughtful. 

Delivering truly exceptional customer experiences is the strongest competitive advantage, and it’s something that is not easily replicated as it’s innate to the very fabric of a company. AI is not a replacement for this hard-to-copy feature of organisational culture - but it can scale it.