Evolving economic models for financial services

Matt Ober is a general partner at Social Leverage. Matt was most recently the chief data scientist at Third Point, where he built the data analytics and technology platform used to enhance the firm’s investment capabilities in equity, structured credit, venture capital and cryptocurrency.

As we approach Money 20/20 starting this weekend, it’s important to reflect on the sentiment that fintech is dead. Spoiler alert: It’s not—it’s just changing. I think fintech is poised for a new beginning, driven by artificial intelligence and the demand for greater efficiency and innovation across financial services. The transition we’re witnessing today—from early internet-era solutions to cloud-based services and now AI-driven platforms—is reshaping the very nature of finance. AI is not only changing the way we approach financial data, but is fundamentally altering business models, compliance, and customer engagement.

AI: The next frontier in fintech

We’ve seen fintech grow from putting existing financial products online to becoming a core element of almost every industry through solutions like cloud infrastructure and mobile-based financial services. Today, AI is powering the next cycle of innovation. 

Traditional banking systems, long reliant on human labor for compliance and customer service, are increasingly looking at AI copilots and agents to take over routine tasks and deliver real-time insights. This shift has the potential to augment the roles of thousands of professionals, transforming the way financial institutions operate.

For example, compliance officers—one of the fastest-growing roles in financial services—are likely to see much of their work streamlined by AI. At large financial institutions, where compliance departments can account for a significant percentage of the workforce, AI can improve accuracy, cut costs, and reduce the time spent on mundane tasks. This is particularly relevant as fintech companies have developed AI-driven tools for communication archiving, with the potential to make these processes more efficient than the traditional manual review of communications across email, social media, messaging platforms, blogs and newsletters.

In addition, we are seeing the work of traditional analysts across hedge funds, asset managers, and investment firms changing quickly. In a world where Excel and Visual Basic skills were once differentiators among candidates, expectations quickly changed to give those with Python or R coding skills a leg up. 

With AI, analysts and investment professionals that are leaning into the AI copilots are finding the most success. Though there are numerous AI copilots for finance popping up every day, the true moat will be those with data.

Rethinking economic models in fintech

One of the enduring myths in the fintech world is that if you become a bank, you’ll be valued like one. This misconception stems from the belief that fintechs with bank licenses will be pigeonholed into bank-like price-to-book (P/B) multiples, which typically cap valuations. But the reality is more nuanced. Fintech disruptors, with their tech-driven business models, can outpace traditional banks in both profitability and growth potential.

Consider the case of Nubank or American Express. Both hold bank licenses yet trade at multiples far higher than traditional banks, thanks to their ability to generate superior return on equity and maintain high margins. This is possible because fintechs often have lower costs thanks to digital distribution, and can generate revenue from both interest and fees. As a result, they have a much higher asset turnover. This makes fintech models not only more nimble but also more attractive to investors seeking long-term growth.

As AI reshapes fintech, a new wave of innovators is emerging, pushing the boundaries of what’s possible in the data-driven world of finance. One of the most exciting entrants is CarbonArc, founded by Kirk McKeown. CarbonArc is creating a consumption-based data marketplace, enabling users to access and pay for the data they need on demand. This model aligns with the growing trend toward flexibility and scalability in how data is consumed, which is critical in an era where AI-driven applications require vast and varied datasets. As the data world continues to evolve, it’s clear that the competition between platforms like Databricks and Snowflake—which are each vying to attract data vendors—will only intensify.