Fintech One-On-One

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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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.
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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.
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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.
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