There’s a quiet shift happening in financial services, and it’s not the one most people talk about. The conversation tends to center on AI replacing traders or chatbots handling customer service. Those are real, but they’re surface-level. The deeper change is structural: finance is becoming a software problem, and AI is the reason.
Here’s what I mean. The core of most financial products — lending, insurance, wealth management — is a decision. Should we lend to this person? How should we price this risk? What portfolio allocation makes sense given these constraints? For decades, those decisions were made by humans using heuristics, or by rules-based systems that encoded those heuristics into code. The models existed, but they were slow to build, expensive to maintain, and brittle in the face of new data.
AI changes the unit economics of decisioning. You can now build an underwriting model that ingests non-traditional data — cash flow patterns, transaction velocity, even how someone interacts with an application — and produces a risk score that outperforms the old FICO-based approach. Not because the math is magic, but because the cost of building and iterating on that model has collapsed. What used to take a team of quants six months now takes a product engineer a few weeks.
This is where it gets interesting from a product perspective. When the cost of making a financial decision drops, you can make more of them. You can underwrite smaller loans that weren’t worth the overhead before. You can offer personalized insurance pricing instead of bucketing people into crude demographic tiers. You can rebalance a portfolio continuously instead of quarterly. The product surface area expands because the underlying computation got cheaper.
I’ve been watching this play out across fintech, and the teams that are winning aren’t the ones with the most sophisticated models. They’re the ones who figured out the right product wrapper around a good-enough model. It’s the same pattern I see in every AI product domain: the model is the commodity, the product decision is the moat.
The risk, of course, is that cheaper decisions become careless decisions. When you can automate underwriting at scale, you can also automate bias at scale. The speed that makes AI useful in finance is the same speed that makes it dangerous. And the regulatory environment hasn’t caught up — most compliance frameworks still assume a human is in the loop somewhere.
But the direction is clear. Finance is moving from a world where the bottleneck was information processing to a world where the bottleneck is product imagination. The models can handle the math. The question is whether we can design financial products that are genuinely better for the people using them, not just cheaper to operate. That’s a product problem, not a model problem. And it’s the most interesting one in fintech right now.
Vamsee Jasti 