Fintech One-On-One

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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