I’m thrilled to sit down with Nicholas Braiden, a pioneering figure in the fintech world and an early adopter of blockchain technology. With his deep expertise in financial technology and a passion for its transformative potential, Nicholas has been a guiding light for startups looking to innovate in digital payments and lending systems. In this conversation, we dive into the evolving landscape of fintech, exploring the aftermath of the so-called ‘fintech winter,’ the rise of AI-native startups, and how cutting-edge technologies like agentic AI are reshaping the industry. We also unpack the strategies that are drawing investor interest and the critical balance between innovation and regulation.
Can you start by shedding light on what people mean by a ‘fintech winter,’ and how long do you think this tough period has impacted the industry?
The ‘fintech winter’ refers to a prolonged period of reduced investment and slower growth in the fintech sector, where funding dried up for many companies after a boom in the late 2010s. It’s been a challenging time, marked by investor caution and a focus on profitability over hype. I’d say this winter has lasted roughly three to four years, starting around 2019 or 2020, when valuations started to cool off after a wave of over-enthusiasm. Many startups struggled to secure capital, and we saw a lot of consolidation in the market as a result.
What indicators are you noticing that suggest this fintech winter might finally be thawing, and how optimistic are you about the recovery?
I’m seeing several encouraging signs. For one, there’s a renewed buzz at industry events like Money 20/20, where investors are actively engaging with startups again. We’re also seeing data showing an uptick in funding rounds, particularly for companies with proven revenue and customer traction. My optimism is cautious but real—I think we’re in the early stages of a recovery, maybe a spring of sorts, driven by innovative players who’ve weathered the storm by focusing on sustainable business models.
Speaking of investor interest, what kinds of fintech companies seem to be grabbing attention right now?
Investors are particularly drawn to scalable fintechs that show real revenue streams, often those generating significant annual income. Beyond the big players, there’s a lot of excitement around embedded finance solutions like banking-as-a-service or payments-as-a-service. Additionally, startups innovating in international payments—especially those moving away from traditional systems and leveraging alternatives like stablecoins or major credit card networks—are getting a lot of looks. It’s about solving real pain points with efficiency.
Let’s talk about AI-native fintechs. How would you define them, and what sets them apart from traditional fintechs or those just tacking on AI as an extra feature?
AI-native fintechs are companies built from the ground up with artificial intelligence at the core of their operations. Unlike traditional fintechs or even newer ones that retrofit AI into existing systems, these startups design their entire business model around AI-driven processes. Think automated onboarding, real-time compliance checks, and predictive analytics baked into every layer. This isn’t just a tool for them; it’s the foundation that makes them inherently more adaptable and efficient.
Could you share a concrete example of how being AI-native provides a competitive edge, perhaps in terms of operational efficiency or enhancing customer experience?
Absolutely. Take customer onboarding, for instance. An AI-native fintech can use machine learning to analyze a user’s data in seconds, verify identities, assess risk, and approve accounts without human intervention. This cuts down wait times from days to minutes, which is a huge win for customer experience. On the efficiency side, it reduces overhead costs since you don’t need a large team manually processing applications. It’s a game-changer for both speed and scalability.
You’ve mentioned that AI-native fintechs are more agile. Can you walk us through how having AI at their core helps them pivot or adapt faster than more established players?
AI-native fintechs can respond to market shifts almost in real time because their systems are built to learn and adjust. For example, if there’s a sudden regulatory change, their AI can analyze the new rules, update compliance protocols, and roll out changes across the platform instantly. Larger, traditional players often rely on legacy systems and manual updates, which can take weeks or months. This agility lets AI-native startups seize opportunities or mitigate risks much quicker.
What exactly is agentic AI, and how does it differ from the more limited, single-purpose AI tools or bots we often see in fintech?
Agentic AI refers to systems that can operate autonomously across multiple tasks or processes, making decisions and taking actions without being confined to a single function. Unlike a chatbot that’s programmed just to answer FAQs or a bot that handles one type of transaction, agentic AI can, say, analyze customer data, suggest personalized financial products, and even execute onboarding decisions. It’s more holistic and proactive, acting almost like a virtual employee with broader capabilities.
Why do you believe agentic AI has the potential to drive more innovation in fintech compared to other AI approaches, and can you give a practical example of its impact?
Agentic AI pushes innovation because it can handle complex, multi-step processes that mimic human decision-making, but at scale and speed. It’s not just automating a task; it’s reimagining workflows. A practical example is in cross-border payments. An agentic AI could assess a transaction, choose the cheapest and fastest payment route—whether through a credit network or a blockchain-based system—and ensure compliance with local regulations, all in one seamless process. That kind of end-to-end efficiency sparks new ways of solving old problems.
Investors seem intrigued by AI-native fintechs for their modern tech and competitive pricing. How does AI enable these startups to keep costs down while still offering high-quality services?
AI automates a huge chunk of operations that would otherwise require human labor, from customer support to fraud detection. This means AI-native fintechs can run with much smaller teams, slashing payroll costs. At the same time, AI optimizes processes—like routing payments or personalizing offers—so they’re delivering tailored, high-value services without the hefty price tag. It’s a lean model that maximizes margins while keeping services accessible and top-tier.
On the topic of lean operations, how does running with smaller teams influence the growth strategies of AI-native fintechs compared to traditional ones that scale by hiring more people?
Traditional fintechs often scale by expanding their workforce to handle more customers or enter new markets, which can slow things down and bloat costs. AI-native fintechs, on the other hand, rely on their tech to scale. Their growth strategy focuses on enhancing algorithms or expanding AI capabilities to manage higher volumes without adding headcount. This lets them grow faster and more flexibly, redirecting resources to innovation or market expansion rather than staffing.
Functionality seems crucial for AI-native startups. How do they manage to stay innovative while also navigating the strict regulatory landscape of financial services?
It’s a tightrope, but AI-native startups often use their technology to their advantage here. They build compliance into their systems from day one, using AI to monitor regulations, flag issues, and adapt processes on the fly. This proactive approach helps them innovate—say, by rolling out new payment features—while ensuring they’re not crossing legal lines. It’s about embedding guardrails into their DNA so they can push boundaries without risking penalties.
A recent report highlighted that many companies using AI see no real boost in earnings. Why do you think so many struggle to get tangible results from AI, and how do AI-native fintechs sidestep this issue?
A lot of companies fail with AI because they’re bolting it onto outdated systems or using it for narrow, isolated tasks without rethinking their broader operations. It’s like putting a sports car engine in a rusty old truck—it just doesn’t work. AI-native fintechs avoid this by designing their entire business around AI from the start. They’re not retrofitting; they’re building systems where AI drives every process, ensuring it delivers real value, like faster growth or lower costs, rather than just being a shiny add-on.
You’ve spoken about ‘mindful trendwatching.’ Can you explain what that means and why it’s so vital for AI-native fintechs to stay attuned to industry shifts?
Mindful trendwatching is about closely observing the evolving dynamics in the fintech space—whether it’s a shift toward local payment systems or changes in international transaction protocols—and strategically deciding which trends to act on. For AI-native fintechs, this is critical because their tech allows them to pivot quickly, but only if they’re paying attention. By aligning their innovations with lasting trends, like the rise of embedded payments, they can position themselves as leaders rather than chasing every fleeting fad.
Looking ahead, what is your forecast for the role of AI-native fintechs in shaping the future of financial services?
I believe AI-native fintechs are poised to lead the next wave of transformation in financial services. Over the next five to ten years, I expect them to dominate areas like payments, lending, and embedded finance by leveraging their agility and efficiency. They’ll likely drive down costs for consumers while introducing hyper-personalized services, fundamentally changing how we interact with money. If they continue to balance innovation with mindful strategy, they could redefine the industry’s competitive landscape entirely.