We sit down with Daniel Broadhurst, Commercial Director at Finova to talk about the technology challenges faced by lenders and the impact of AI
Can you share a bit about your background in the fintech sector?
I spent nearly a decade working in tier one banks on the lending side before making the jump into fintech. Along the way, I worked with a range of lending technology providers, supporting everyone from startup neobanks to long-established high street brands. It gave me a front row seat to the real challenges modern lenders face: operational pressures, shifting cultures, and what really adds value.
In fintech, I saw up close how banks wrestle with technology systems every day. The biggest lesson I took away is that technology, when used correctly, is a powerful enabler. It’s about helping banks and building societies become better versions of themselves through genuine partnerships. Every tech decision should ultimately make things better both for the lender and the borrower at the end of the journey.
What are your main responsibilities as Commercial Director?
My focus is straightforward: I want to position Finova for long-term success in a rapidly evolving market. We already have a solid foundation. Our technology underpins one in five mortgage originations in the UK. We support over 60 lenders. The numbers speak for themselves. It’s a strong platform to build from, but there’s still significant room to grow our customer base.
Customer expectations are shifting, regulation is becoming more demanding, and financial institutions are navigating operational pressures they’ve never faced before. My role is to ensure we’re fully aligned to those needs, both for the customers we serve today and the customers we want to bring on board tomorrow. That means staying close to the market, scaling the right commercial capabilities, and actually listening to what lenders need, not what we assume they need.
Of course, I’d be lying if I said it wasn’t about winning in a competitive market! Too many tech firms build products based on guesses rather than genuine insight. We explore every possible avenue, test every possible solution, to help our clients confront the real-world challenges of today’s market. When you build what customers truly want, everyone wins. That includes our business, our clients, and the customers they serve.
Why do you think the UK’s mortgage market has struggled to keep pace with fintech innovation?
“I would challenge the premise that the mortgage market has ‘struggled’. It has just evolved at a different pace, and in all fairness, there are good reasons for that. Mortgage lending is complex by design. Every lender faces different risk appetites, product designs, and regulatory demands, all sitting on top of strict capital requirements. It’s not an industry where you can ‘move fast and break things.’
“But borrower expectations have changed dramatically. People now expect everything to be instant and intuitive. When lenders couldn’t keep up, the pressure to innovate intensified, not for the sake of new technology, but because customers demanded it.”
What’s the biggest tech challenge still holding back innovation in financial services?
Data. More specifically: data quality, structure, and accessibility. Most financial institutions are sitting on years of inconsistent data trapped in siloed systems. You can have world-class AI, but if the underlying data is messy, fragmented, or inaccessible, it’s useless.
I see a lot of excitement about new technology from financial institutions, but not enough focus on the basics - duplicate records, mismatched formats, incomplete fields, systems that don’t speak to each other. That’s what’s really slowing innovation, not the tech itself.
When your data quality isn’t right, everything else slows down. Decisions take longer, customers repeat themselves, and experiences become fragmented. Solving the data problem is where the real step change will come from.
Do you think the impact of AI has been exaggerated?
Yes, the hype is loud. But no, the potential isn’t exaggerated. AI isn’t new, and frankly, parts of it have been around for years. Where it will have a genuine impact in the near term is in document processing, data extraction, and pattern recognition. In buy-to-let underwriting, for example, AI can analyse rental agreements and bank statements in seconds, massively accelerating applications.
But the real bottleneck isn’t AI, it’s data. If the data is inconsistent, unstructured, or inaccessible, even the smartest AI won’t deliver good outcomes. Financial institutions need to fix their data foundations before anything else.
Ultimately, AI only matters if it helps institutions make faster, fairer decisions. Borrowers don’t care if it’s AI or a spreadsheet; they just want a quick answer and a good experience. The limiting factors now aren’t the algorithms; they’re trust, regulation, and, most importantly, data quality.
Beyond mortgages, what do you see as the biggest opportunity for innovation in other specialist finance areas, such as commercial real estate or bridging loans?
The real step change will come from embedded finance delivered through a single platform where every stakeholder works off the same data. We’ve got great tech, good processes, and emerging AI, but the data is still scattered across countless systems. Shuffling that data around is what slows everything down.
At the moment, borrowers upload the same information multiple times, and different stakeholders re-key it into different systems. What if the borrower only uploaded documents once? And all authorised parties - brokers, lenders, valuers, solicitors, insurers - could access the same data securely and instantly? For specialist finance, especially bridging and commercial real estate, this would be game-changing. Speed matters, and shared data removes the friction that slows deals to a crawl.
It’s less about reinventing individual systems and more about building the infrastructure that links them all together.
What is one headline that you would be delighted to see in 2026?
At Finova, we talk a lot about 'segmentation of one’, a world where every borrower gets a fully personalised offer built around their individual circumstances, behaviour, and risk profile.
Right now, most lenders operate with crude segmentation. It’s first-time buyers versus remortgaging borrowers, the employed versus the self-employed. But imagine every application receiving a dynamically optimised offer: rates, LTV, features, and even the journey, tailored to that specific person.
The technology already exists. We’ve got Open Banking data, real-time credit information, behavioural patterns, and property insights. The issue isn’t the data, it’s the ability to operationalise it at scale. When a lender can price every borrower based on their actual risk and relationship value rather than a standard rate card, everything changes. That’s lenders genuinely serving customers as individuals, not categories. And it means borrowers finally get pricing that reflects who they are - not which arbitrary bucket they’ve been dropped into.
