Venture banking 3.0: How Stifel is rebuilding trust after Silicon Valley Bank

When Silicon Valley Bank collapsed in March 2023, it took with it a bank that had quietly become infrastructure for the startup ecosystem, holding the majority of the venture debt market and serving as the one relationship founders and fund managers trusted for everything from a wire transfer to an introduction. Bigger banks and a wave of neobanks moved fast to fill the void. Three years later, the question is whether either one actually replaced what SVB was. Today I'm joined by Katya Kohen, managing director at Stifel, where she leads investor coverage nationally for the firm's venture and fund banking group. Katya's path into banking runs through venture, not around it. She built and exited a tech company, invested as an angel in immigrant founders, built her own accelerator, and spent years at Techstars before joining Stifel as, in her words, its first non-traditional banker. That mix of operator, investor, and now banker shapes what she calls venture banking 3.0, a founder-first model built after the SVB meltdown. We get into what actually broke in the founder-bank relationship, how Stifel has grown its venture banking book toward $12 billion in commitments, and why venture debt has become essential capital for AI companies given how capital intensive the category has become. Let's get into it.

Most banks seem to chase small business customers as one segment among many. Live Oak Bank was built around a bet that specializing beats generalizing — and it started about as narrow as a bank can start, lending only to veterinarians. Today, Live Oak has grown that thesis into 40 verticals, holds the top spot among all SBA 7(a) lenders by dollar volume, and has done it all without a single branch. My guest is BJ Losch, president of Live Oak Bank. Live Oak has never had a physical location, yet its people travel the country to sit across the table from customers a branch down the street never would. Now the bank is layering AI onto that model, cutting loan approval-to-close timelines from over 100 days toward a two-week target, and thinking hard about what it means to stay high-touch while going high-tec. We talk about the theory of verticality, where automation should and shouldn't touch the credit decision, and why BJ sees AI as an accelerant of Live Oak's strategy rather than the strategy itself.

Amy Avery, Managing Director, Analytics, Modeling and Insights, took her job at Bank of America because of a number. When she interviewed at Bank of America, she was told that the bank interfaced with, at the time, 67 million clients. "Gosh, that's so much information," she remembers thinking. "Think about what you could do with that.” She started in January 2020. Two months later, the pandemic made that abstraction very literal: the bank suddenly needed to know, in real time, how its customers were doing, thinking, and coping. Avery's job was to figure out how to answer that. Michelle Boston, Head of Data Management Technology & Enterprise Architecture, arrived by a different route entirely. She built her career in enterprise technology, rose to CIO of a startup that was eventually built and sold, and came to Bank of America first as a contractor to lead an information architecture practice. “Data has always kind of been in my blood,” she said. At Bank of America, she works at a scale few other organizations have and builds the platforms that serve as the enabling force for Avery's work. Despite a very different set of starting points, the two describe a partnership that has essentially erased the line between their jobs. "We probably know each other's jobs better now than before generative AI showed up”, Avery said, because the pace of the last two years has forced her strategy team and Boston's engineering team to make decisions in near lockstep. Listen to the full episode to hear how Avery and Boston have built a shared language across the two functions, and how they're stress-testing it against a technology cycle that seems to wait for no one.

Welcome to The Editors’ Room, a new Tearsheet Podcast series where Editor-in-Chief Zack Miller and Managing Editor Sara Khairi take the conversations that usually happen behind the scenes about our biggest stories and put them on the record. There isn’t a rehearsed interview or carefully choreographed panel answers. It’s just two editors comparing notes, challenging each other’s takes and trying to make sense of what is actually happening in financial services. Think of it as pulling up a chair after the meeting ends. It's the stuff we usually debate after the calls end: what a new product actually means, which industry trends have legs, and where the hype gets ahead of reality. Raw, conversational, and occasionally accompanied by a blooper. For our inaugural conversation, the topic was AI and, more specifically, what would you happily delegate to AI and what would you never hand over? From there, we got into the bigger question of how enterprise AI is taking shape. On that first question, Zack’s line is creativity. AI can handle planning and logistics, and he uses it as a kind of editorial sparring partner, asking questions, challenging ideas, and pushing him to dig deeper. But the creative judgment stays human. Sara draws the line at decision-making. Take an expensive laptop: she'll happily let AI compare the options, but she wants to be the person who clicks buy. It turns out that tiny distinction – AI can help make the decision, but shouldn't necessarily make it – is becoming a much bigger question in financial services.

Every account payable and account receivable department runs on the same friction: invoices chased four or five times, payments late more than 30% of the time, and credit control teams that can run 20, 30, even 50 people deep at a mid-sized corporate. Virtual cards were built to solve exactly that problem, and the growth numbers show it — spend is projected to hit $17.4 trillion by 2029, according to Juniper Research. Today I'm joined by Marc Pettican, global head of corporate solutions at Mastercard, who's spent decades working both sides of the payments ecosystem, from the merchant acquiring side to leading commercial cards. We get into what's really driving virtual card growth beyond the macro tailwinds, how MasterCard balances network economics between buyers and suppliers, and the build-versus-partner calculus behind expanding from travel into verticals like fleet and logistics, healthcare, insurance, and marketplaces. We also dig into embedded finance and the challenge of staying visible in the stack even as payments become invisible to the end user — plus where Mastercard sees its right to win in agentic payments, account-to-account transfers, and stablecoins. Marc closes with his advice for commercial card heads at mid-sized banks over the next twelve months.

For most small and midsize businesses, financial operations still look a lot like they did a decade ago. Bills get keyed in manually. Receipts pile up. W-9s get chased down at tax time. While the tools have multiplied, the work hasn't gone away. “Most finance teams work in an incredibly manual way,” says Michael Cieri, Chief Product Officer at BILL. “There's a ton of work done by finance professionals that could be automated, and could actually be done better through the use of technology.” The gap between the promise of modern financial software and the day-to-day reality of running the books at a small business is something BILL has spent nearly two decades trying to close. The company processes over 1% of US GDP in payments and has moved more than a trillion dollars across its platform – a scale that gives it both a data advantage and a particular sense of accountability. When you're handling that volume of transactions for the long tail of American businesses, the stakes of getting automation wrong are very high. Cieri joins us on the show to talk through where BILL's product thinking stands today: how Cieri's team decides when to take big swings versus make incremental improvements, how it builds and validates AI features for a high-trust domain.

Welcome to the Tearsheet Podcast, where we explore financial services together with an eye on technology, innovation, emerging models, and changing expectations. I'm Tearsheet's editor in chief, Zack Miller. Fintech just lived through four distinct ages — pioneers, growth-at-all-costs, the 2021-22 hype cycle, and the brutal reset that followed. Now we're in a fifth: bigger, more profitable, and more disciplined than any version that came before it. Stripe's reportedly eyeing a six-figure-billion IPO. Fintech listings tripled investor appetite this year. And yet talk to anyone who lived through 2021 and they'll tell you this doesn't feel anything like that boom. To make sense of that contradiction, I sat down with the authors of a new joint report from McKinsey and QED Investors — two firms that sit on opposite sides of the table from the fintechs they study. Max Flötotto is a senior partner at McKinsey, where he leads the firm's global retail banking practice and coordinates its fintech work across Europe. Mike Packer is a partner at QED, leading growth-stage investing globally for a firm that's been backing fintech since its earliest days, nearly two decades now. We dig into the report's biggest findings: why the simplest version of banking — collecting deposits, making loans — is structurally at risk if customers start letting their own AI agents shop for the best rate; why fintechs have, for the first time, actually overtaken incumbents on trust in Europe, even as banks have closed much of the product gap; and the massive spread in how seriously banks are actually taking AI, from "talking about thinking about it" to rebuilding their entire operating model around it. We close with each of them picking the one trend, out of six in the report, they think matters most for the next decade. Max, Mike, welcome to the show.

Debt consolidation has always rested on a promise lenders couldn't verify. A borrower takes out a HELOC, says they'll pay off their credit cards, and the lender hands over the cash and hopes for the best. Credit bureau data lags by 30 days. There's no mechanism to confirm the debt actually got retired. And a significant share of consolidation borrowers end up re-accumulating balances — leaving lenders with paper that performed worse than expected and borrowers worse off than before. Figure and Method set out to close that loop. Figure is the largest non-bank HELOC originator in America, a public company on the Nasdaq running a two-sided capital marketplace on blockchain rails. Method is a financial connectivity API that gives lenders real-time access to a borrower's full liability picture — and the ability to pay those liabilities off directly at the moment of funding. Together, they've built what they're calling verified debt consolidation: a closed-loop system where the lender doesn't hope the debt will be paid — they know it will be. Today I'm joined by Mit Shah, co-founder and COO of Method, and Rod Albuyeh, who leads AI at Figure and is something of a boomerang — he was at Figure from 2020 to 2022, left, and returned in January to a company that had transformed around him. We talk about what the data actually shows, what happens when this capability travels across Figure's 380 white-label partners, and whether verified debt consolidation is a premium feature or the future of the category.

Small business banking has always had a structural problem: the companies that hold your money and the companies that build your financial software have been two different things. One moves the money, the other tracks it, and small business owners are stuck in the gap between them. A new generation of fintechs has been trying to fix that, but most are still building horizontal tools for every business everywhere. The more interesting bet is vertical, going deep into the specific workflows of a particular industry and automating them completely. My guest today is Victor Cardenas, co-founder and CEO of Slash, a business banking platform that started with teenage sneaker resellers and has grown into a $1.4 billion company by doing exactly that — building industry-specific financial products that legacy banks will never prioritize. Slash processes nearly $3 billion in stablecoin payment volume annually, and the company has been doing serious work rethinking how AI fits into both how they operate internally and what their customers experience.

Most people leave money on the table every time they swipe — not because they're careless, but because the credit card rewards ecosystem is genuinely complicated. Thousands of cards, millions of merchants, shifting bonus categories, buried benefits. The promise of AI is that it can do that optimization work invisibly, in the background. Today I'm joined by Tikue Anazodo, co-founder and CEO of Kudos — an AI-powered smart wallet that tells you which card to use at checkout, recommends cards based on your spending habits, and layers on additional rewards on top of what your cards already earn. Kudos has raised over $17 million, is backed by QED Investors, and was named to Forbes' Fintech 50. Tikue, welcome to Tearsheet.