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Fintech One-On-One

Peter Renton

Fintech is eating the world. Join Peter Renton, Co-Founder of Fintech Nexus and now an independent fintech media and events consultant, every week as he interviews the fintech leaders who are leading the transformation of financial services. If you want to understand what the future will look like for lending, payments, digital banking and more, tune in to Fintech One-On-One.

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Thursday · 34 min
Why Upstart Is Building a Bank From Scratch Instead of Buying One With CEO Paul Gu
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
September 3 · 34 min
What is Missing From Instant Bank Payments With Arpit Goel, CEO of Root
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
August 27 · 28 min
Acquiring Banks and Creating an Underwriting Moat in Mexico With René Saúl, CEO of Kapital
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
August 20 · 33 min
Why Enova Wants a Bank Charter, Not Just Cheap Deposits with CEO Steve Cunningham
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
August 13 · 31 min
Why Banking Fundamentals, Not Technology, Decide Who Survives in Sponsor Banking With Amanda Swoverland, President of Hatch Bank
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
August 6 · 34 min
A New Intelligence Layer for Community Lenders with Mike de Vere, CEO of Zest AI
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
July 30 · 31 min
Why Card-Linked Installments is a Better Form of BNPL With Nandan Sheth, CEO of Splitit
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
July 23 · 32 min
The $70 Billion Escheatment Problem for Banks, Fintechs and Crypto With Allen Osgood, CEO of Eisen
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
July 16 · 33 min
Why Accounts Receivable Is Fintech's Biggest Untapped Market With Caitlin Leksana, CEO of Fazeshift
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
July 9 · 34 min
Why the Best Fintech Companies Are Staying Private With Sahej Suri, Founder of Blue Dot Investors
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. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
July 2 · 31 min
Why Full Autonomy Beats Co-Pilots for AI in Banking with Dimitri Masin, CEO of Gradient Labs
Dimitri Masin was one of the first 30 employees at Monzo, where he led AI and data science as the bank grew from 30 to 4,000 people. That vantage point showed him where the real work in financial services still lives: the manual, repetitive customer operations running behind the app. In 2023, he co-founded Gradient Labs to automate that work with fully autonomous AI agents, and the company now serves more than 30 fintech and financial services customers. In this conversation, we get into why co-pilots can quietly degrade quality and compliance, why Dimitri believes full autonomy is the safer path, and the story behind what may be the largest known AI agent deployment in banking. What We Covered From Google to one of the first 30 people at Monzo The second half of the fintech transformation Why customer operations never got reinvented What GPT-4 unlocked at the start of 2023 Putting banks on autopilot Sitting as an orchestration layer over existing systems The 15% customer experience uplift over human teams Why cost savings are more nuanced than people expect How bank implementations and bake-offs actually work Why co-pilots can degrade quality and compliance The case for full autonomy over a human in the loop Benchmarking agents against the human team, not perfection Redeploying staff instead of cutting headcount The largest known AI agent deployment in banking Why banks aren't seeing productivity gains yet The build-it-ourselves mindset shift A five to ten year view of the transformation How the US bake-off culture plays to a specialist's advantage Key Takeaways The overlooked opportunity in banking is not the app experience but the manual operational work behind it: customer support, AML, fraud, KYC, onboarding, and screening. Co-pilots can backfire. When suggestions are right 90% of the time, people start accepting them blindly, which degrades quality and compliance in the other 10%. No agent is correct 100% of the time, and that is the wrong bar. The right question is whether the system beats the human team it replaces, which becomes the benchmark. Automation has not meant layoffs at any of Gradient Labs' customers. Teams get redeployed to complex, higher-empathy work like vulnerability and financial difficulty cases. The bottleneck on transformation is not the technology, which has existed since GPT-4, but how slowly organizations diffuse and adopt it. Dimitri's horizon is five to ten years. About Dimitri Masin Dimitri Masin is the CEO and co-founder of Gradient Labs, a London-based startup building autonomous AI agents that run customer operations for regulated financial services companies. Before founding the company in 2023 with two former Monzo colleagues, he was among the first 30 employees at Monzo, where he led AI, data science, financial crime, and fraud as the bank scaled to roughly 4,000 people. He started his career at Google. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
June 25 · 33 min
How Navan Coded Company Policy Onto the Card to Kill the Expense Report with Yuval Refua
Yuval Refua is the Chief Product Officer at Navan, the global travel and expense platform he joined seven years ago when it was still just a travel booking service. Since then, he has built out its payments and expense products from the ground up, turning the company policy that used to live in a PDF into code that runs on the card itself. This conversation matters because T&E is one of the most universally disliked workflows in business, and Navan is rethinking it from scratch just as AI and agentic commerce start to reshape how companies spend. What We Covered Falling in love with credit cards at American Express Why Navan started as a travel-only booking service The reconciliation pain that led to launching a card Coding company policy directly onto the card Real-time approval the moment you swipe Why travel-first beats procurement-first Context as the key to managing distributed spend Going global with VAT, GST, per diems and mileage The e-invoicing wave hitting more countries The GTA model for revealing complexity gradually The Expense Admin Companion and recommended actions From single approvals to bulk to full automation The Visa partnership and the Connect product Waymo for travelers, Formula One for finance Key Takeaways The expense report exists to answer a question that company policy already settled. Coding that policy onto the card removes the work instead of automating it. Starting from travel gives Navan context (where the employee is, why they are there, who they are visiting) that procurement-first tools lack, which makes per-employee limits far smarter. Going global is less about features and more about mastering country-by-country tax, e-invoicing, per diem and mileage rules. The path to full automation runs through trust. Navan moves finance teams from a single recommended action, to bulk approvals, to hands-off automation, which is also how it intends to handle agentic spend. About Yuval Refua Yuval Refua is Chief Product Officer at Navan. He started two companies of his own early in his career before moving into fintech and product management at Thomson Reuters, then American Express, where he developed a deep love for credit cards and the rails behind them. He joined Navan around seven years ago and has built out its payments and expense products from the ground up. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
June 18 · 52 min
Fintech Revealed: Deep Dive on Vertical Fintech with Increase and Tekion
This episode is part of our occasional Fintech Revealed series, where we do an extended deep dive into one topic with two industry experts. The topic today is vertical fintech, and I am joined by Matt Hennessy, the Business Lead at Increase, the modern banking infrastructure company, and Jamie Fox, the General Manager of Fintech at Tekion, the AI-native cloud platform that runs the entire business for auto dealerships across the US, Canada, and the UK. Tekion built its embedded banking on Increase, so the two of them give us both sides of the same story: the platform that lives inside the dealership and the infrastructure that connects it to the banking system. We get into the surprisingly large money flows inside a single dealership, why paper checks still beat instant rails for many operators, how compliance and trust get engineered into the product, and just how big this embedded banking opportunity gets. What We Covered What vertical fintech is and why it matters now The money flows hiding inside a single car dealership Why outbound dealer spend is roughly 2x inbound Operating account vs. ledgering account adoption paths Dealer-to-dealer payments as a ledger change with zero rail fees Instant rails: RTP, FedNow, and Request for Payment The persistence of paper checks and the cost to operationalize them Direct Fed access vs. layers of middleware Compliance as code, codified into the product Building trust in building blocks Where agentic payments and "know your agent" fit in How large the embedded banking opportunity ultimately gets Key Takeaways Owning the financial system of record inside core operating software is the defensible position in an age when light "systems of engagement" can be replicated with AI. Outbound payments, not inbound, are the bigger prize: US auto dealerships pushed out roughly $1.3 trillion in 2024, about 2x what they took in. The barrier to instant rails is education, not technology. Many dealers do not know RTP or FedNow exists, or that they can pay a vendor any day of the week. Trust cannot be launched all at once. Holding a dealer's operating cash is a different level of trust than processing a payment they can fall back on, and it is earned in building blocks. For the founding story and more about Increase, check out my conversation with CEO and Founder Darragh Buckley from last year. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
June 11 · 31 min
How Edge Focus Is Bringing Quant Trading Precision to Consumer Lending With CEO Elliott Lorenz
Elliott Lorenz took an unusual path into consumer lending, moving from applied mathematics and high-frequency trading into the business of pricing credit risk. Today he is the CEO and co-founder of Edge Focus, a technology-enabled private credit firm that sits between consumer lending platforms and the institutional investors who want to deploy capital into the asset class. In this episode, Elliott explains how the firm's credit engine works, why speed is its biggest edge, and how he reads the recent wave of criticism aimed at private credit. What We Covered From engineering and applied math to high-frequency trading What Michael Lewis's Flash Boys got right and wrong about HFT Spotting an edge in LendingClub's public loan data Turning a data-science hobby into Edge Focus The Origin credit engine and how it makes decisions Expanding a lender's credit box with an orthogonal view of credit Modeling with a single month of payment history Updating a credit model within a day The Lens portfolio analytics tool Where alpha comes from beyond the underwriting model Fraud and asset liability mismatch in private credit Building the EDGEX ABS shelf and partnering with Fortress Proving ML models are free from bias Where consumer lending goes over the next few years Key Takeaways Edge Focus competes less on having a single better model and more on combining technology, capital, and platform relationships in one package, which Elliott calls the firm's "big unlock." The firm can incorporate even a single month of payment history into its models and push an update within a day, letting it react to macro shifts faster than firms that wait 12 to 24 months for data. Most of the recent private credit criticism falls into two buckets, fraud and asset liability mismatch, and Elliott sees the fraud cases as largely idiosyncratic and the redemption problems as a function of investors misjudging illiquid assets. Because Edge Focus invests its own capital alongside partners rather than acting as a pure technology vendor, its incentives are tied directly to loan performance. About Elliott Lorenz Elliott Lorenz is the CEO and co-founder of Edge Focus, a technology-enabled private credit firm focused on consumer lending. He trained as an engineer and applied mathematician, earned a master's in finance from Princeton, and spent several years in high-frequency trading before bringing those modeling techniques into consumer credit in 2013. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
June 4 · 33 min
What's Finally Changing to Help Catch More Financial Crime With Andrew Davies of ComplyAdvantage
Andrew Davies has spent more than three decades fighting financial crime, starting with sanctions screening tools for central banks in the mid-1990s and arriving at ComplyAdvantage after nearly 16 years at Fiserv. He sits at the center of one of the most consequential questions in financial services: can we finally move the needle on financial crime detection after decades of catching less than 2% of what's laundered globally? ComplyAdvantage serves more than 3,000 enterprises across 75 countries with its AI-native Mesh platform. If you want to learn more about the founding story and their early days, check out my podcast with founder Charlie Delingpole from 2019. What We Covered Why the industry has historically caught less than 2% of money laundered globally How the money laundering economy ranks as the world's third largest at an estimated $5.6 trillion The evolution from sanctions screening to FRAML to multi-dimensional financial crime risk The Mesh platform and what a unified financial crime system means for compliance teams Cassie, the agentic AI analyst automating customer screening investigations How 90% of compliance work was historically spent chasing false positives Real-time payments compliance and the risk-based approach to payment screening The SEPA Instant Payments challenge and batch screening against the EU journal Stablecoins, unhosted wallets, and the compliance infrastructure gap FATF's finding that stablecoins represent 84% of illicit crypto transaction volume Data sharing consortiums as the next inflection point in fighting financial crime The network problem at the heart of money laundering and terrorist financing Key Takeaways The money laundering economy is estimated at $5.6 trillion, making it the third largest in the world, above Germany, yet we detect less than 2%. Agentic AI tools like Cassie are designed to eliminate false positives so human analysts only work cases that genuinely warrant their expertise. Data sharing consortiums, where organizations contribute to shared detection models, represent the most promising path to materially improving financial crime outcomes. Stablecoins create real compliance risk at the unhosted wallet layer, the Bank of England has floated a ban, while the US is unlikely to go that route, leaving a gap. About Andrew Davies Andrew Davies is the Global Head of Financial Crime Compliance Strategy at ComplyAdvantage. He began his career in the mid-1990s building sanctions screening tools for central banks and large financial institutions, and spent nearly 16 years at Fiserv in their financial crime division before joining ComplyAdvantage. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
May 28 · 29 min
Why Embedded Payments is a Retention Strategy for Vertical SaaS with Joshua Silver, CEO of Rainforest
Joshua Silver has spent two decades in embedded payments. Before co-founding Rainforest, he built Patient Co, a healthcare payments business scaled to billions in processing volume and tens of millions of patients, then spent several years consulting with software founders on building their payments programs. Rainforest is payments as a service, purpose-built for vertical SaaS — and in this conversation Joshua makes a compelling case that embedded payments is not just a revenue opportunity but a competitive moat. What We Covered Why vertical SaaS companies are still leaving money on the table with embedded payments The gap in the market Rainforest was built to fill How payfac as a service works and who it is designed for Why the number of registered payfacs is shrinking, not growing The $5 billion volume threshold for when becoming a full payfac makes economic sense How Rainforest differentiates from Stripe and Adyen for vertical SaaS platforms Vertical-specific risk models versus general-purpose tools Rainforest's real-time ledger and what it unlocks for complex payment structures Adding PayPal and Venmo for untapped vertical SaaS markets Expanding into Canada and building the playbook for international growth How AI is being used across the business and the rising threat of AI-driven fraud What success looks like for Rainforest in the next five years Key Takeaways Embedded payments builds a moat. Joshua's closing point is the sharpest: once merchants are running their money through your software platform, competitors face a much harder job dislodging you. Payments isn't just a revenue line — it's a retention strategy. Vertical-specific risk models matter enormously. Stripe and Adyen have to serve everyone, so their risk tooling is built for the lowest common denominator. Rainforest has built models tuned to individual verticals — lawn care looks different from HVAC, which looks different from nonprofit donations — and it takes the fraud liability rather than passing it to the platform. The $5 billion payfac threshold is the new reality. A decade ago the rule of thumb was around $1 billion in card volume. Regulatory and compliance burdens have risen so sharply that Joshua now puts the threshold at $5 billion with line of sight to $10 billion before it makes economic sense to go full payfac. A real-time ledger is a competitive differentiator. Most legacy processors are batch-based, settled overnight on mainframes. Rainforest's ledger is real-time, enabling split payments, franchise fee hierarchies, and complex billing structures that batch systems simply cannot support. About Joshua Silver Joshua Silver is co-founder and CEO of Rainforest, a payments-as-a-service company purpose-built for vertical SaaS platforms. Before Rainforest, he co-founded Patient Co, scaling it to billions in healthcare payments volume before a sale, and subsequently consulted with software founders on building their payments businesses. He has been working in embedded payments for twenty years. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
May 21 · 35 min
How Figure Is Cutting Mortgage Costs from $12,000 to $1,000, with CEO Michael Tannenbaum
Michael Tannenbaum became CEO of Figure in early 2024, taking over from founder Mike Cagney and leading the company through its September 2025 IPO. In this conversation, we get into the mechanics of how Figure's blockchain-based platform competes with Fannie Mae and Freddie Mac, what it actually takes to cut mortgage origination costs from $12,000 to $1,000, and where the real opportunities in tokenization lie. What We Covered Taking over as CEO from Mike Cagney and the Big Rocks framework How Figure describes itself: building the future of capital markets on blockchain The B2B partner network and how it compares to Fannie Mae's function Cutting mortgage origination costs from $12,000 to $1,000 and 45 days to five Why Figure competes directly with Fannie Mae and Freddie Mac How blockchain eliminates third-party diligence and prevents loan double-pledging The Figure Connect marketplace and its rapid growth since June 2024 Where tokenization adds real value — and where it doesn't YLDS: Figure's SEC-registered yield-bearing stablecoin and its role in capital markets The timing and mechanics of Figure's September 2025 IPO Building a rate-agnostic business across different macro environments Three growth areas: consumer mortgages, Democratized Prime, and on-chain equities Key Takeaways Figure's origination platform and its capital market are the same system — you can't separate them, and that's the competitive moat. Tokenization only creates liquidity when the underlying assets are standardized and fungible; putting unique assets on a blockchain doesn't conjure buyers. The recent fraud cases involving double-pledged loans (Tricolor, First Brands, MFS) have turned blockchain's immutability from a skeptic's objection into a selling point. And Figure is running at what Michael calls the rule of 150 — 100% year-over-year growth at 50% margins — in one of the most rate-sensitive and entrenched markets on earth. About Michael Tannenbaum Michael Tannenbaum is the CEO of Figure, a blockchain-based capital markets company he took public on Nasdaq in September 2025. Before Figure, he was an early executive at both SoFi (Chief Revenue Officer) and Brex (COO), and sat on the Brex board when it was acquired by Capital One. He began his career in investment banking at J.P. Morgan. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
May 14 · 27 min
Fixing the Broken Appraisal Model in Asset-Backed Lending With Thomas Galbraith, CEO of Barkr
Thomas Galbraith is the CEO and co-founder of Barkr, an AI-driven valuation platform for asset-backed lending. He spent his early career in high net worth insurance at AIG and AXA, where he grew comfortable with the challenge of pricing hard-to-value assets. That thread ran through every role he held until it crystallized into a company built around a simple but structural problem: in asset-backed lending, appraisers give you a price and then spend the rest of their report telling you they're not responsible for it. Barkr is built to change that. What We Covered Thomas's background in high net worth insurance at AIG and AXA How a common thread across luxury assets led to founding Barkr Starting with fine art and private jets before expanding to other asset classes The two-part failure in traditional appraisals: accuracy and absence of liability How Barkr pairs an AI valuation with a contractual performance warranty The progression from Lloyd's of London to AXA to Munich Re $2 billion in covered valuations and what patience actually means in this business GPUs as a surprisingly durable and long-lived collateral asset class How Barkr finds clients, from pavement pounding to Nvidia referrals Monthly mark-to-market on hard assets throughout a loan's life Building a domain-specific LLM with human review in the loop Plans to build an in-house insurance vehicle to unlock capacity Key Takeaways Traditional appraisal firms hedge their liability by design. Page one is the price; the rest of the report is the disclaimer. Barkr's contractual warranty flips that model by standing behind the number. Barkr's data on GPU durability challenges the conventional narrative. Chips five and seven years old are still generating revenue and still have meaningful resale value, which changes the risk calculus for lenders considering AI infrastructure as collateral. Augmenting, not replacing, is the right positioning for valuation technology. Barkr actively encourages clients to keep using their existing appraisers and treats third-party appraisals as additional data inputs that improve their own accuracy. Building a reinsurance relationship takes years. Barkr worked through Lloyd's, then AXA, before landing Munich Re, and each step required demonstrating proof of concept at the prior level first. About Thomas Galbraith Thomas Galbraith is the CEO and co-founder of Barkr. He began his career in high net worth insurance at AIG and AXA before founding Barkr to bring accountability and AI-driven accuracy to asset valuation in the lending market. Barkr has covered approximately $2 billion in valuations across art, private jets, vehicles, and GPUs. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
May 7 · 31 min
Building the Bank-Grade Ledger That Payments Infrastructure Was Missing With Patricia Montesi, CEO of Qolo
Patricia Montesi didn't start her career in payments, she started it in car rental. After nine years at Alamo and National Rent-A-Car, she was recruited into fintech with zero industry experience. That outsider perspective became her edge, and she never let go of it. Today, she's the CEO and co-founder of Qolo, a payments infrastructure platform that combines card issuing, money movement, and a bank-grade ledger on a single API-first stack. What We Covered How nine years in car rental shaped Patricia's outsider approach to payments Getting recruited into Wild Card Systems with no payments background, and why that fresh lens became an advantage The fragmentation problem at the heart of payments infrastructure and why point products create hidden complexity Qolo's three-product suite: Quantum Ledger, Qascade money movement, and Qinetic card issuing Why Qolo isn't quite a side core, it overlays and integrates with existing bank cores rather than running in parallel Rail agnosticism and why Qolo still supports checks in 2026 The dual go-to-market: commercial banks and B2B fintechs, same platform, different vernacular How the Synapse collapse changed the ledger conversation for banks and fintechs alike Winning KeyBank in a competitive RFP against much larger players, and launching virtual account management in nine months How banks are using Qolo to protect commercial deposits from modern non-bank competitors AI inside Qolo: from Glean to Claude, and their internal "Turning Hours into Minutes" program 130% year-over-year growth and 142% net revenue retention Key Takeaways The moat problem: Patricia set out to build a company where customers stay because of the value delivered, not because switching is too painful. That philosophy shaped every product decision at Qolo. Ledger first: Most point-product fintechs have basic ledgers that only support one rail. Qolo's bank-grade dual-entry forward-posting ledger underpins every rail, making reconciliation and real-time money visibility a solved problem rather than a vendor management challenge. Synapse's legacy: The debacle forced banks and fintechs alike to ask harder questions about who actually owns the ledger and where money sits at any given moment. Qolo had been making that argument for years before the market was ready to hear it. Bank as distribution: KeyBank and Huntington aren't just clients — they're strategic investors using Qolo to defend their commercial deposit base against modern non-bank alternatives. About Patricia Montesi Patricia Montesi is CEO and co-founder of Qolo, a payments infrastructure company she built from the ground up after more than 20 years in the industry. She started her career at Alamo and National Rent-A-Car before being recruited into fintech with zero payments background — an outsider perspective she has held onto ever since. At Qolo, she and her team built the ledger, money movement, and card issuing stack as first-party infrastructure, without relying on third-party processors underneath. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
April 30 · 31 min
The Case for AI as a Revenue Driver in Financial Infrastructure with Chris Walters, CEO of Finastra
Chris Walters is the CEO of Finastra, one of the largest financial software companies in the world, serving over 7,000 banks globally including 45 of the world's top 50. He joined the company a little over a year ago, bringing an unusually broad background spanning consulting, Bloomberg, The Weather Company, and several other technology businesses. This is a wide-ranging conversation about where Finastra is headed and why the conventional narrative around AI and software disruption misses something important. What We Covered Chris's path from consulting to Bloomberg, The Weather Company, and beyond What attracted him to Finastra and the perception versus reality gap he set out to close How he spent his first 90 days listening to customers and internal teams before deciding direction The portfolio narrowing strategy, including divestitures of Treasury, Capital Markets, and student lending Finastra's core focus areas: lending, payments, and universal banking Growth vectors within an existing base of 7,000+ banks, including geography expansion, cross-sell, and data The AI center of excellence and why dedicated ownership changes the pace of deployment Internal AI use cases: an HR chatbot and automated sales approvals Operator Assist, a new product that uses AI to surface and resolve failed payments Agentic AI in mortgage origination, targeting documentation discrepancies Why Finastra views AI as a growth accelerant, not a cost-cutting tool, and why not all software faces the same disruption risk Community bank caution around modernization and why the economics will eventually force full core replacements Key Takeaways Companies that are systems of record with long-duration enterprise agreements are far less exposed to AI disruption than the public markets currently assume. The distinction matters, and Chris makes a clear case for why Finastra sits in the less-exposed category. Dedicated AI ownership changes everything. Spreading AI enthusiasm across everyone's partial attention generates ideas but not scalable execution. The center of excellence model exists precisely to fix that. Community bank core modernization is inevitable but slow. The banks most likely to win that market are those that can make transitions nearly frictionless, not those with the most advanced technology. At $7 trillion in daily payments routed through Finastra's systems, the probabilistic nature of LLMs is not a minor technical detail. Chris's post-recording observation about where AI fits and where it doesn't is one of the more clear-eyed takes you'll hear from a CEO in this space. About Chris Walters Chris Walters is the CEO of Finastra, which he joined a little over a year ago. Before Finastra, he held CEO and COO roles at a range of public and private technology companies, including The Weather Company and a public wealth management and software business. He also spent seven years in consulting and held senior roles at Bloomberg. Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes

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