Fintech One-On-One

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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