Paul Gu dropped out of Yale in 2010 to join the first class of Thiel Fellows, spent time at the quant fund D.E. Shaw, and then co-founded Upstart on a simple premise: the techniques Wall Street uses to price corporate risk should work at least as well on consumer credit. Fourteen years later, he took over as CEO from co-founder Dave Girouard, and six weeks after that, the OCC granted conditional approval for Upstart Bank. This conversation covers what has actually changed at the top of the company, why Upstart went for a full de novo national charter rather than buying an existing bank, and where AI is reshaping the parts of lending that nobody talks about.

What We Covered

• Dropping out of Yale for the first Thiel Fellowship class
• What D.E. Shaw taught him about applying quant techniques to personal finance
• The income share agreement idea that brought the co-founders together
• What changed when he took over as CEO on May 1
• Losing the balance of a three-founder culture, for better and worse
• The core personal loan business and the future prime borrower
• The trifecta of growth, profitability and credit performance
• Auto and home, and the race to contribution margin positive
• What conditional approval from the OCC actually means
• Why a de novo charter rather than acquiring a bank
• Where the existing bank and credit union partners land after Upstart Bank opens
• AI in loan verification and servicing, beyond the underwriting model
• What happens when AI agents start applying for loans on people's behalf
• Ninety-one percent automation and whether 100% is reachable
• The case that fixing the cost of credit makes most Americans 10% wealthier

Key Takeaways

• Upstart went de novo rather than buying a bank because acquiring one means inheriting someone else's loan book, underwriting practices and operational history, and the whole pitch depends on being able to stand behind every decision inside the bank when a regulator asks.
• The bank changes who originates, not who funds. Upstart Bank will become the principal originator, but Gu is explicit that the company is not becoming a large, equity intensive balance sheet business, and the bank and credit union partners keep buying the assets.
• The next AI wins are in verification rather than underwriting. A HELOC can carry several thousand dollars of human verification cost because county property records are non-standard and non-deterministic, which is exactly the kind of work a generalized reasoning agent is suited to.
• Gu welcomes a world where AI agents apply for loans on borrowers' behalf, because agents have unlimited time to search and no brand loyalty to defend, which favors the lender with the best rate rather than the biggest marketing budget.

About Paul Gu

Paul Gu is co-founder and CEO of Upstart, the AI lending platform he started in 2012 after dropping out of Yale as one of the first Thiel Fellows and spending time at the quantitative hedge fund D.E. Shaw. He spent most of his career as the technical half of the founding partnership, running product, engineering and machine learning before taking general management of the auto and home businesses. He succeeded co-founder Dave Girouard as CEO on May 1, 2026.

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Arpit Goel built his first company, Gamma, on a simple pitch: legacy data-loss-prevention tools took nine months to show value, and Gamma got customers there in two weeks. Palo Alto Networks acquired Gamma in 2021. Now Goel is running the same play in a completely different industry. Root is a payments orchestration layer that lets enterprises move money bank-to-bank in about five seconds, with no intermediary ever holding the cash. In this conversation, Arpit explains why he thinks the US is finally close to a tipping point on instant payments, why Root never takes custody of the money it moves, and how the company handles banks that can't yet receive instant payments.

What We Covered

• Growing up in India, and the ADHD diagnosis that pushed him toward IIT Delhi
• Why he calls himself an "ignorant" founder rather than an experienced one
• The nine-months-to-two-weeks wedge that built Gamma, and why Root uses the same one
• Discovering the payments inefficiency by reading through ADP's 10-K
• Why 40% of US SMBs don't accept cards, and it isn't about the fees
• The dual pressure of RTP and FedNow that made 2024 the right moment to start Root
• The heart, arteries, and capillaries analogy for how Root fits into the banking system
• What actually happens to money in the five seconds between sender and receiver
• Why reliability, not transaction scale, is the hard engineering problem in payments
• How Root handles banks that can't receive RTP or FedNow
• Where stablecoins fit into a bank-rail-agnostic platform
• Why Root wants to be the pipes underneath the industry, not the brand

Key Takeaways

• SMBs refuse cards mostly because of settlement delay, not fees — restaurants earning their week's cash on a Saturday night don't see it until Tuesday, right when they need it most to restock.
• Root never takes custody of funds. Money moves directly between the sender's and recipient's own bank accounts, and Root charges a fee on top rather than earning float.
• Reliability, not transaction volume, is the hard engineering problem — Root has built retry and fault-tolerance systems, using the workflow engine Temporal, so a bank outage doesn't have to mean a failed payment.
• Arpit sees instant payments as a market that hasn't tipped yet but is close — RTP and FedNow now operating together create sustained pressure that neither rail created alone.

About Arpit Goel

Arpit Goel is the founder and CEO of Root. He holds a computer science degree from IIT Delhi and a PhD from Stanford, and previously founded Gamma, a data-security company acquired by Palo Alto Networks in 2021, where he went on to lead product for the data-security business.

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René Saúl spent seven years running an offline agricultural lending business in Mexico before selling it and pouring the proceeds into Kapital, a bet that the future of B2B fintech in Latin America belonged to companies willing to become regulated banks. Today Kapital is the largest B2B fintech in the region, and René is the only founder in the space who has bought not one but two banks, one of the deals agreed to on a napkin.

What We Covered

• Why the future of fintech is regulated, and why that was a contrarian call in 2021
• René's seven years running an offline agricultural lending business in Mexico
• The founding thesis behind Kapital's one-stop B2B banking ecosystem
• How Mexico's electronic invoicing system became Kapital's underwriting moat
• The "red car theory" of spotting opportunities before they arrive
• Buying Banco Autofin on a napkin, and growing its deposits from $150 million to $400 million in three months
• Acquiring the banking, brokerage and payments assets of Grupo Financiero Intercam
• Building instant, 24/7 cross-border payment rails on top of SWIFT
• Closing the small business financing gap with AI-native underwriting
• Why Mexico is becoming a cornerstone of America's AI manufacturing boom
• The limits of banking an economy that still runs largely on cash
• Kapital's growth numbers and its path to a dual listing in New York and Mexico

Key Takeaways

• Mexico's electronic invoicing mandate hands Kapital more than 50,000 data points per customer, a seven-year head start on underwriting that competitors using third-party providers cannot easily close.
• For large enterprises and cash-strapped SMBs alike, a banking license, not a slicker app, is what earns the trust needed to hold their money and their cash flow.
• Opportunities have to be hunted, not waited for. Kapital tracked potential bank acquisitions for years so it could move in days when the Autofin deal appeared.
• Staying liquid and profitable before either acquisition is what let Kapital move fast when the opportunity came, rather than scrambling to raise capital under pressure.

About René Saúl

René Saúl is the co-founder and CEO of Kapital, which he built after selling an offline agricultural lending business that financed berry and avocado exporters in Mexico and Peru. Under his leadership, Kapital has grown into a licensed financial group serving more than 300,000 customers across Latin America, with more than $5.3 billion in assets and two bank acquisitions behind it.

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Enova International has spent two decades using machine learning underwriting to serve consumers and small businesses who sit outside prime bank criteria, and its pending $369 million acquisition of Grasshopper Bank would give it a national charter for the first time. Steve Cunningham became CEO in January 2026 after nearly a decade as the company's CFO, following earlier stops as a bank regulator at the FDIC and as chief risk officer at Discover. He joins the show to explain what a fully digital lender looks for in a nonprime borrower, why credit quality looks solid in his portfolio right now, and how he's answering the senators and state attorneys general who want regulators to block the Grasshopper deal.

What We Covered

• Steve's path from FDIC regulator to Capital One, Harley-Davidson, and Discover
• Moving from the CFO chair to the CEO chair six months in
• Enova's brand portfolio: CashNet, NetCredit, and OnDeck
• Underwriting nonprime and near-prime consumers versus underwriting small businesses
• The lift Enova's proprietary models get over a plain FICO or VantageScore
• Why all their products use different underwriting models
• What Enova's weekly vintage data shows about the health of the consumer
• Why gas prices matter less to consumer spending than headlines suggest
• How Enova is using generative and agentic AI across the business
• The real thesis behind the Grasshopper Bank acquisition (see my podcast with CEO Mike Butler)
• Steve's response to the senators and state attorneys general opposing the deal
• What banking-as-a-service adds to Enova's roadmap
• Where Enova wants to be by 2030

Key Takeaways

• Enova's NetCredit yields and losses aren't outliers when benchmarked against what banks themselves report to the FDIC each quarter, Cunningham argues, pushing back on the "predatory" framing critics apply to the company.
• The Grasshopper deal is primarily about simplifying a patchwork of direct state licenses and bank partnership arrangements, not chasing cheap deposits, though the deposit base is a welcome bonus.
• Because Enova's consumer loans repay every two weeks or faster, the company sees shifts in borrower behavior in its own vintage data well before those shifts show up in macro statistics.
• Small business underwriting at Enova is built around the health of roughly 900 different industry codes rather than a borrower's personal credit, making it a fundamentally different discipline than consumer underwriting.

About Steve Cunningham

Steve Cunningham is CEO of Enova International, a role he took on in January 2026 after nearly a decade as the company's CFO. He previously served as chief risk officer and treasurer at Discover, CFO of Harley-Davidson Financial Services, held senior finance roles at Capital One, and began his career as a bank regulator at the FDIC.

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Very few people in this industry have sat in all three of the seats that matter in the bank-fintech story. Amanda Swoverland started as a compliance examiner at the Federal Reserve Bank of Minneapolis, spent nine and a half years at Sunrise Banks rising to Chief Risk Officer, then joined Unit as its fourth employee and Chief Compliance Officer. Six months ago she became President of Hatch Bank, a California-chartered ILC that works exclusively with fintech lending partners. She still describes herself as a banker at heart, and this conversation is a good explanation of why that matters more now than it did five years ago.

What We Covered

• From Fed compliance examiner to bank president
• Why she was never the department of no
• Learning product and sales inside a fintech infrastructure company
• Hatch Bank's credit-only model, with no deposits
• The five lending verticals Hatch focuses on
• Going deep with a few partners instead of diversifying across 30
• What a fintech gets from a small sponsor bank that scale cannot offer
• Lifting a BSA/AML consent order in under a year
• "Maturing for scale" as the theme of her first six months
• Using AI internally without sending agents out into the wild
• Why the quality of founders approaching sponsor banks has gone up
• The direct versus not direct debate after Synapse
• Why every fintech should have a second bank partner
• Where AI is genuinely working in compliance today
• DIDMCA, state charters and the usury patchwork
• What separates the sponsor banks that survive the next cycle

Key Takeaways

• The "direct versus not direct" framing that took hold after Synapse is, in Amanda's view, a distraction. If a bank has a program, the bank is in charge of it, whatever technology sits in the middle and whoever is acting as program manager. Everything else is a question of how you oversee it, not who is accountable.
• The next failure will not look like Synapse, because that particular gap has been closed. What worries her is banks that never learned the fundamentals: liquidity, credit oversight, BSA/AML, and how a multi-party lending program behaves when the cycle turns and payments stop arriving on time.
• A second bank partner is good for the fintech and good for the bank. Concentration risk cuts both ways, and Amanda actively introduces her own clients to other banks she trusts, and is happy to be someone else's second bank.
• Compliance is heading toward 100 percent sampling. Amanda thinks the days of testing a selected sample of transactions or complaints are ending, provided you test the system, watch the outputs, and keep a human in the loop.

About Amanda Swoverland

Amanda Swoverland is President of Hatch Bank, a San Marcos, California ILC that works exclusively with fintech lending partners across home improvement, small business, clean energy, student lending and healthcare financing. She began her career as a compliance examiner at the Federal Reserve Bank of Minneapolis, spent nine and a half years at Sunrise Banks where she became Chief Risk Officer, and then five and a half years at Unit as Chief Compliance Officer, joining as the company's fourth employee. She was named to Forbes' 2026 list of the women shaping fintech infrastructure and banking strategy.

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Mike de Vere runs Zest AI, a company that has been applying machine learning to credit underwriting for over two decades, starting with some of the largest banks on the planet and now serving a large share of the credit union market. Since his last appearance on the show three years ago, Zest has expanded well past underwriting into fraud detection and portfolio management, tied together by an intelligence layer and a generative AI companion called LuLu. Mike makes a specific argument in this conversation: machine learning still makes the credit decision, generative AI makes the feedback loop faster, and the real advantage available to community financial institutions is a willingness to pool what they know.

What We Covered

• Zest today, from underwriting to fraud to portfolio management
• Why the intelligence layer is what makes an ecosystem
• Starting with Discover, Citi and Freddie Mac, then moving down market
• LuLu, named after a corgi, and what she actually does
• Safety and soundness as the first use case for most institutions
• Replacing quarterly reports that used to take weeks
• Peer benchmarking versus building your own data lake
• Collective intelligence across 2,000 credit models in production
• Why generative AI has no role in making the credit decision
• Shrinking model refit cycles from 18 months to daily evaluation
• Zest customers versus non-customers on growth, delinquency and efficiency
• Cash flow underwriting, and why generic national models fail
• Zest Protect and fighting AI-powered fraud with AI
• The two objections that come up most in sales conversations
• Takeaways from the IQ AI Lending Forum in Santa Fe

Key Takeaways

• The performance gap is measurable. Comparing Zest customers to non-customers across 2024 and 2025, Mike says his customers grew 16 times faster, ran roughly 20 points lower on delinquency, and were 501 basis points better on efficiency ratio.
• Generative AI belongs around the credit decision, not inside it. Zest still uses supervised, locked-down machine learning models for underwriting, because a regulator will ask you to explain the decision. What generative AI changes is the speed of evaluation, from an 18-month refit cycle to daily.
• Comparison is where the value sits. A lender looking only at its own data lake has visibility on itself and nothing else. LuLu is built to normalize performance data across institutions so a chief lending officer's instinct can be checked against thousands of real policy instances rather than one career's worth of experience.
• Community lenders have a structural advantage they underuse. The credit union industry holds roughly $2.4 trillion in assets. If it acted as one institution, it would be bigger than Wells Fargo, and unlike the big banks these institutions are actually willing to share.

About Mike de Vere

Mike de Vere is the CEO of Zest AI, the AI lending technology company that has been doing machine learning in credit since well before AI became a standard fintech conference track. He came to Zest from a career in data and consumer insights, with leadership roles at J.D. Power, The Harris Poll and Nielsen. Zest now touches $5.6 trillion in assets under management, and by the end of this year expects one in three credit union members to have their consumer loans decisioned with its technology.

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Nandan Sheth has spent 25 years in payments, building three growth companies along the way, including Harbor Payments (sold to American Express) and Acculynk (sold to First Data/Fiserv). He now runs Splitit, which takes a different path than most buy now, pay later providers: instead of originating a new loan, it turns the credit a consumer already has on their existing card into an installment plan, with no underwriting, no social security number, and no new debit card for repayments. With agentic commerce infrastructure being built in real time, Nandan argues that a frictionless installment option is exactly what merchants need to avoid being commoditized on price inside an LLM shopping platform.

What We Covered

• Three growth companies across 25 years in payments
• What attracted Nandan to Splitit from Fiserv
• Card-linked installments with no underwriting or new loan
• The card loyalist versus the credit needy
• $3.5 trillion of unused credit sitting on US cards
• Merchant-funded 0% economics and where the budget comes from
• A $1,300 average order value versus $250 to $300 for standard BNPL
• Point of sale through the Samsung Wallet integration
• Backing Google's Universal Commerce Protocol
• The overlooked small business to large supplier B2B use case
• Chargebacks, repudiation, and who carries the risk in agent-led purchases
• Splitit Go for the face-to-face services economy

Key Takeaways

• BNPL is really two markets, not one. Card loyalists want rewards, protections, and habit, while the credit needy want a new line of credit. Nandan thinks both get served, but by different products.
• The economics work because the merchant treats it as marketing spend. About 98% of Splitit's volume is a merchant-funded 0% plan, priced comparably to a percentage-off promotion, and it lifts average order value roughly four times over standard BNPL.
• In agentic commerce, price and delivery speed are the easiest things for an LLM to compare. A 0% installment option gives merchants a third lever that is not pure price competition.
• The B2B version may be the stronger use case. Small business owners face both a time problem and a working capital problem, which is a sharper reason to hand off buying to an agent than a consumer shopping for a polo shirt.

About Nandan Sheth

Nandan Sheth is the CEO of Splitit, the card-linked installments platform. He moved to the US from the UK 25 years ago and has spent his entire career in payments and fintech, including running e-commerce and omni-channel commerce at Fiserv. He previously built Harbor Payments, acquired by American Express, and Acculynk, acquired by First Data/Fiserv.

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Escheatment is a $70 billion problem hiding in plain sight: every state, territory, and dozens of countries have laws that hand dormant and unclaimed accounts over to the government after three to five years of inactivity. Allen Osgood, co-founder and CEO of Eisen, left a five-and-a-half-year run as a payments product manager at Coinbase to build the compliance infrastructure that helps banks, brokerages, and crypto platforms reunite customers with their money before the states ever claim it. In this conversation, Allen makes the case that crypto is about to collide with escheatment rules written in the 1960s, and that most institutions have no idea how large their own dormant balances really are.

What We Covered

• What escheatment actually is and how the state-by-state rules work
• The $70 billion states are holding for more than one in seven Americans
• Missingmoney.com and what happens after money is remitted
• Ohio's fight over using unclaimed property to fund a football stadium
• The Walter story: an E-Trade Amazon account liquidated to Delaware
• What counts as a "dormant" account and why logins matter
• Where Eisen plugs into the escheatment process
• Why reactivation beats remittance, and the Binance.US 48% case study
• Why institutions are blind to their largest dormant balances
• The 12-to-24-month gap where accounts just age untouched
• Displacing big-four spreadsheets with a single pane of glass, forecasting, and access controls
• Data volume as the hardest engineering problem, and where AI earns its keep
• The Claims Portal and QR-code reactivation
• Why crypto makes escheatment far more painful, from volatility to dust
• The coming wave of crypto liquidations and the tax problem
• Channel strategy with the cores like Fiserv, and the road to 1099 and tax reporting

Key Takeaways

• The best escheatment outcome is no escheatment at all. Eisen's real value is retention: keeping customers, deposits, and assets in the institution rather than shipping them to the state.
• Institutions routinely underestimate their exposure. One prospect thought it had 10,000 accounts about to escheat, the real number was 100,000. The disconnect sits between the compliance team and the data on the ground.
• Crypto changes the stakes. States generally require liquidation, so a dormant token gets sold, creating an unwanted taxable event and, if the market rips afterward, another Walter waiting to happen.
• Stale data is the enemy. The information that comes due for escheatment is by definition three to five years old, so address enrichment (LexisNexis, Socure, USPS NCOA) and early engagement are what actually move the reactivation numbers.

About Allen Osgood

Allen Osgood is the co-founder and CEO of Eisen, a compliance operations platform that automates escheatment and account offboarding for financial institutions. Before founding Eisen, he spent about five and a half years as a payments product manager at Coinbase, where he first ran into the strange world of unclaimed property and stayed through the company's IPO.

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Accounts payable has produced multiple billion-dollar companies, yet its mirror image, accounts receivable, remains almost entirely manual at most enterprises despite decades of software spend. In this episode, Caitlin Leksana, co-founder and CEO of Fazeshift, explains why AR has remained unsolved and how her company's AI agents are changing that. A mechanical engineer turned BCG consultant turned founder, Caitlin came to the problem the hard way, doing her own AR by hand at a previous startup, and her outsider's view of a stubborn back-office chore is exactly what makes the conversation worth your time.

What We Covered

• A million AR analysts doing manual work in the US
• Why accounts payable got solved and AR did not
• The leverage imbalance between AP and AR departments
• The swivel chair problem and fragmented data
• $200 million in unapplied cash on one balance sheet
• Fazeshift as a context layer, not a rip-and-replace
• Why traditional SaaS and if-then logic could never scale AR
• The collections, cash application, and AR inbox modules
• Human in the loop and building trust when AI touches money
• Training agents on historical data and tribal knowledge
• From Y Combinator to a Series A led by F-Prime
• The vision for the context layer and autonomous finance

Key Takeaways

• AR is the inverse of AP, and every bill is someone else's invoice, so the market is at least as large and mostly uncaptured.
• The real unlock is not the AI model but unifying fragmented data across the ERP, bank, CRM, and inbox into a single context layer.
• Human in the loop with full auditability is what earns risk-averse finance teams' trust, and it is how agents move toward full automation over time.
• Some of the best unsolved startup problems are the ones furthest removed from an engineer, because no one with the tools to fix them ever felt the pain.

About Caitlin Leksana

Caitlin Leksana is the co-founder and CEO of Fazeshift, a San Francisco startup building AI agents for accounts receivable. She earned bachelor's and master's degrees in mechanical engineering from Georgia Tech, advised Fortune 500 companies at BCG, and earned her MBA at Harvard Business School before founding a crypto marketing startup and then Fazeshift. The company went through Y Combinator's Summer 2024 batch, raised a $4M seed led by Gradient Ventures, and announced a Series A led by F-Prime in 2026.

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Sahej Suri is the founder of Blue Dot Investors, a late-stage growth equity firm that invests exclusively in fintech across both primaries and secondaries. Before Blue Dot, he built his career at J.P. Morgan, TPG, and as chief of staff to Nigel Morris at QED Investors. In this conversation, Sahej explains the scrappy origin story of the firm, the overlooked opportunity in fintech secondaries, and his new report with FT Partners on the coming fintech liquidity supercycle, including the finding that the top 100 private fintechs now out-earn the top 100 public ones.

What We Covered

• Sahej's path from J.P. Morgan to TPG to QED
• The 2008 recession and why access to financial services stuck with him
• The happenstance origin story of Blue Dot
• Why fintech is closer to biotech than to generalist tech
• The gap in the market for late-stage fintech specialists
• Why the top 10 names dominate secondary market activity
• Finding undervalued companies outside the marquee names
• The "Liquidity Supercycle" report with FT Partners and how it came together
• Why the top 100 private fintechs out-earn the top 100 public ones
• The state of the IPO window and the SpaceX bellwether
• Why the 2025 IPO cohort cleared a much higher bar
• The have versus have-nots dynamic in fintech fundraising
• The Blue Dot dinner series and building community
• His AI thesis and where the value creation will land
• A 10-year view on fintech as an asset class

Key Takeaways

• The best fintech companies are now private, and on the top 100 they out-earn their public peers on revenue, a finding Sahej says had never been put on paper before.
• Fintech rewards specialists. Banking, payments, capital markets, and insurance are almost different worlds, and most investors who piled in during 2021 without that depth are no longer around.
• The IPO window is real but conditional. The 2025 cohort was roughly three times the size on revenue and more profitable than historical norms, and the near-term window hinges on how bellwether listings perform.
• Sahej's bet on AI value creation is not the startups or the large AI labs, but the scaled fintechs that already own distribution and customer trust.

About Sahej Suri

Sahej Suri is the founder and Managing Partner of Blue Dot Investors, a New York-based late-stage growth equity firm investing exclusively in fintech across primaries and secondaries. He previously worked at J.P. Morgan in the financial institutions group, at TPG in growth equity and buyouts, and as chief of staff to Nigel Morris at QED Investors. Blue Dot came out of stealth in early 2026 and manages roughly $100M in assets, with a team of six and around 30 advisors. Peter is an advisor to Blue Dot Investors.

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