Fintech One-On-One

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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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.
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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.
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