Is fintech innovation dead, or are we on the cusp of an AI-driven revolution? As community banks and credit unions scramble to stay relevant, two contrasting views emerge on the state of innovation in financial technology and artificial intelligence.”
This week, we look at two views of innovation and how the business models of fintech and the broader change from AI are underwhelming innovation leaders but also have the potential to deliver well beyond even the peak hype we may be experiencing now.
1. Cynical Fintech
Dan Davies of the UK outsourcing credit analyst firm Frontline Analytics writes a slightly tongue-in-cheek hot take on the state of fintech innovation. His post A Cynic’s Guide To Fintech takes a mostly grim view of the underlying business models of emerging fintech companies and why they fail to impress.
One of his best-rated ideas:
Fintech business model #7. Getting your act together with respect to an industry standard where the industry has conspicuously failed to do so
In principle, it should be impossible to seriously compete against the banks in international monetary transmission. They all ought to have good networks of correspondent banks, to be able to handle their nostro, vostro and loro accounting and so to be able to convert foreign exchange and send payments via extremely cheap central bank systems. In fact, the correspondent networks are often lousy, meaning that payments have to take dozens of extra unnecessary steps, and the accounting systems are reconciled too slowly. Even the small number of banks that know what they are doing don’t feel like they’re in a competitive industry, so they don’t act that way. And this is only one of a number of areas — Apple Pay ought to have been a huge wakeup call to the banks not because it was a brand new way of making payments, but because it was basically the only fully satisfactory implementation of an existing industry standard. Again, there is a certain degree of long term business risk inherent in building your company around the inability of the banking industry to get its act together, but there are lots of very vulnerable areas here too.
Viability rating — (5/5)
To be fair here, Dan’s livelihood is based on providing human capital in the form of credit analysts, so his less-than-sanguine outlook on fintech is understandable. But, in the context of where the mountain of VC has gone into fintech over the past five years, the results have been less than inspiring and less than transformative to banking and financial services in general.
While Davies offers a cynical view of fintech innovation, the landscape of technological advancement extends beyond just financial services. As we shift our focus to the broader impact of AI, we see both challenges and opportunities that may reshape not just banking, but entire enterprise architectures.
2. Enterprise Philosophy and The First Wave of AI
This week, Stratechery’s Ben Thompson offered a less bitter take on innovation in general. His piece examines broader architectural issues and concludes that early ROI and the resulting focus in the short term will come more from human replacement than human augmentation.
Looking at the messaging of CRM leader Salesforce and its CEO Marc Benioff at the company’s Dreamforce conference last week:
Ben comments:
Agents aren’t copilots; they are replacements. They do work in place of humans — think call centers and the like, to start — and they have all of the advantages of software: always available, and scalable up-and-down with demand.
To the extent that is right, then, the biggest opportunity is in top-down enterprise implementations. The enterprise philosophy is older than the two consumer philosophies I wrote about previously: its motivation is not the user, but the buyer, who wants to increase revenue and cut costs, and will be brutally rational about how to achieve that (including running expected value calculations on agents making mistakes). That will be the only way to justify the compute necessary to scale out agentic capabilities, and to do the years of work necessary to get data in a state where humans can be replaced. The bottom line benefits — the essence of enterprise philosophy — will compel just that. And, by extension, we may be waiting longer than we expect for AI to take over the consumer space, at least at the scale of something like the smartphone or social media.
Ben’s analysis, as expected, goes much deeper. Still, his main point is that the full promise of AI has an important bottleneck in the access and organization of enterprise data. Looking at the depth of work that information leader Palantir has done on just that problem exposes the depth of work that is still required to take advantage of the generative AI capabilities available today.
As community banks and credit unions navigate this complex landscape of innovation, the lessons from both Davies’ cynicism and Thompson’s enterprise perspective are clear. While flashy fintech solutions may not always deliver on their promises, the transformative potential of AI is undeniable. However, realizing this potential requires more than just adopting new technologies – it demands a fundamental rethinking of data architecture and business processes.
For smaller financial institutions, the path forward may lie in striking a balance: being cautious about overhyped fintech solutions while strategically investing in AI capabilities that can genuinely enhance customer experiences and operational efficiency. The key will be to focus on innovations that solve real problems and create tangible value, rather than chasing the latest trends.
That’s a wrap for this week. To quote Ronnie Van Zant, “What song is it you want to hear?”. Click below to let us know how we did: