Purpose Driven FinTech

60 million Brazilians can't get a credit card. For years, that meant subscription businesses simply couldn't bill them. And the moment a new payment rail shows up, everyone assumes it just steals volume from cards. This week I speak with Sebastian Fantini, Product Director of EBANX, and he makes the commercial case that a local recurring rail grows the market instead of shifting it.EBANX connects global merchants to local payment methods across more than 20 emerging markets. It now processes transactions for 26% of Pix users in Brazil and 38% of all Pix Automático transactions. In Pix Automático's first year, 64% of the people paying with it were brand new customers, not cardholders who switched. Hotmart saw a 32% lift in customer retention after turning it on.We get into why card subscriptions lose 20 to 30% of recurring revenue to involuntary churn and how a QR rail recovers it, why mandated adoption is what made Pix stick where other countries' schemes stalled, the go-to-market that took EBANX to 38% market share, and EBANX's plans to replicate success in SEA.What you'll learnWho the 60 million credit cardless Brazilians actually areWhy adding a local payment method can grow your addressable market instead of cannibalizing card revenueHow mandated adoption made Pix stick where other countries' instant payment schemes stalledWho actually funds the 3 to 5% discount merchants give you for paying with Pix, and why they choose toThe go-to-market that took EBANX to 38% of Pix Automático transactionsWhy card subscriptions lose 20 to 30% of recurring revenue to churn, and how a recurring QR rail claws it backHow B2B and SaaS billing is quietly moving onto a rail built for consumersWhy you can win a customer with marketing and still lose them at the payment checkoutTimestamps00:00 — 60 million people, no credit card02:26 — Who actually can't get a credit card in Brazil04:37 — How EBANX connects global merchants to local payers06:14 — Pix versus Pix Automático, and why recurring QR matters09:17 — Is Pix Automático cannibalizing cards, or growing the market?13:05 — Why Pix succeeded where other countries' schemes stalled21:33 — Who funds the Pix discounts23:50 — The go-to-market behind 38% market share29:36 — Why the checkout screen decides conversion35:01 — B2B and SaaS are paying by QR39:24 — Reducing involuntary churn with Pix Automático43:46 — Expanding into Southeast Asia and handling the FX👉Connect with Sebastian:EBANX: https://www.ebanx.comLinkedIn: https://www.linkedin.com/in/sebastian-fantini-a0a1b970/👉Connect with MonicaLinkedIn: https://www.linkedin.com/in/monicamillares/Purpose Driven FinTech. English: https://www.youtube.com/@monica_millares/videosPurpose Driven FinTech. Spanish: https://www.youtube.com/@MonicaMillares_Español/videosDisclaimer: This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past, or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion on social media.

For centuries, a voice was proof. You heard your banker, your child, your CEO, and you knew. Not anymore. A voice can now be cloned from a few seconds of public audio. The problem is not that voice disappears, it is that voice becomes easy to manufacture. This week I speak with Anat Goldstein, Founder of FinOptima, whose whole argument is that no single signal, not a voice, not a password, not a device, can prove who is on the other end of a call anymore.FinOptima builds explainable fraud detection for the institutions the industry skipped: community banks and credit unions, which face the same deepfake threats as the top 20 banks on a fraction of the budget and with no in-house fraud team. The numbers are not abstract. Fraud losses are projected at 40 billion dollars. In one case, criminals deepfaked senior executives on a video call and moved around 25 million, and the employee who sent it was not careless. We get into why fraud now happens inside the live call rather than before it, what regulators mean when they demand a reason for every blocked transaction, and why the harder problem coming is not detecting fakes but proving which interactions are real. Anat also traces the move from a 20-year banking career to founding a company, validated through customer discovery before any code, and built one hard decision at a time.What you'll learn:Why a voice can no longer prove identityWhy one signal only creates suspicion, and how voice, behavior, device and transaction history together are what create confidenceWhy fraud detection has to happen during the call, not after the money has movedWhy community banks and credit unions carry the same threat as the largest banks with almost none of the resourcesWhen it makes sense to partner rather than build fraud infrastructure in-houseWhat "explainable" fraud AI actually means to a regulator, and why a risk score on its own no longer holds upWhat fraud looks like once AI makes it cheap enough for anyone to run, and why trust infrastructure has to come firstHow Anat found and validated the idea through customer discovery, and what she means by resilience built one decision at a timeTimestamps00:00 — Cold open: your voice is no longer proof 02:24 — 40 billion in losses, and a 25 million dollar deepfake call 04:13 — How do you know the caller is real? 05:21 — Why community banks and credit unions are the target 07:09 — Why today's fraud tools miss it: they work in silos 08:10 — Fraud that happens inside the live call 08:59 — What explainability means to a regulator 12:08 — One signal creates suspicion, many create confidence 17:17 — Build versus buy, and the first 90 days 20:16 — Leaving a 20-year banking career to found a company 24:37 — Resilience, built one decision at a time27:50 — When fraud gets cheap: good AI versus bad AIConnect with Anat: 🔗 FinOptima: https://www.linkedin.com/company/finoptima-solutions/ 🔗 LinkedIn: https://www.linkedin.com/in/anat-goldstein/Connect with Monica 🔗 LinkedIn: https://www.linkedin.com/in/monicamillares/ 🔗 Purpose Driven FinTech: https://www.youtube.com/@monica_millares/videos 🔗 Purpose Driven FinTech. Spanish: https://www.youtube.com/@MonicaMillares_Español/videosDisclaimer: This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion in social media.

Banks spend millions on carbon footprint. Banks have an engagement problem. Something is not working! This week I speak with Mathias Boissonot, Founder and CEO of Handprint and he makes a commercial case for a different model: instead of tracking what your customers do wrong, quantify what they're building right.Handprint powers ocean regeneration programs for banks like DBS, including coral reef restoration and mangrove planting that customers own, watch grow, and choose to fund with their spend. The result isn't just goodwill. It's an 875% increase in customer engagement compared to legacy carbon programs, and $23 million a year in verified ecosystem value returned to society.We get into why Mathias measures success by a metric most founders have never heard of: the "phoenix" over the unicorn, and the sales and founder lessons he's learned building the company from scratch.What you'll learnWhy carbon offsetting and "your carbon footprint" features fail to change customer behavior, and what the psychology actually isThe handprint model: quantifying positive impact instead of negative impact, and why that flips the entire customer relationshipHow banks turn nature engagement into a growth engine: referral programs with co-owned impact, nature cashback instead of standard cashbackWhy 62% of customers would switch banks over purpose misalignment, and 52% already haveThe phoenix metric: measuring company success in ecosystem value generated, not just valuationMathias's biggest founder mistakes, and the best sales advice he's ever received: "implicate the pain"Timestamps00:00 — Cold open: has carbon offsetting failed?04:40 — What Handprint actually does06:55 — Phoenix vs. unicorn: a new metric for success09:00 — $23 million in ecosystem value, every year18:30 — Why donation and carbon-guilt features don't work24:40 — Kill the cashback war: nature cashback instead27:30 — From cost center to growth engine: the 875% number32:30 — Building a company from Bali35:30 — Every mistake we could have made, we've made41:35 — Implicate the pain: the best sales advice he's received44:00 — Resilience, mindset, and having fun in the painConnect with Mathias:🔗 Handprint:https://handprint.tech/🔗 LinkedIn: https://www.linkedin.com/in/boissonot/Connect with Monica🔗 LinkedIn: https://www.linkedin.com/in/monicamillares/🔗 Purpose Driven FinTech. English: https://www.youtube.com/@monica_millares/videos🔗 Purpose Driven FinTech. Spanish: https://www.youtube.com/@MonicaMillares_Español/videosDisclaimer: This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion in social media.

What does it actually mean when an AI agent completes a purchase on your behalf? I sit down with Anatole Baboukhian, Executive Lead, Corporate Responsibility & Strategic Advocacy, and Lucy Anderson, Head of Asia Solutions, SMB Product; colleagues at Global Payments on opposite sides of the business, policy and product. We start with a plain english definition of agentic commerce and where it fundamentally changes from how we pay today. From there we talk through consent, trust, and the liability question every fintech founder needs an answer to before they build. We had a ton of fun, talk about Taylor Swift and bill payments!What you’ll learn:Agentic commerce splits into two models: instructed vs. fully autonomousKYB still isn't solved. KYA (Know Your Agent) is next, and it's harder: identity, mandate, scopeLiability isn't one framework: it depends where in the chain the failure happensBoring, high-friction use cases (utility switching, ticket buying) will win before exciting ones (travel)Why Marketing is about to need a second audience: agents, not just peopleTIMESTAMPS00:00 – What agentic commerce actually means01:27 – The two use cases nobody separates07:19 – What regulators are really asking09:32 – KYC → KYB → KYA: the trust gap nobody's closed16:42 – The three-layer KYA framework: identity, mandate, scope18:44 – Why the "boring" use cases will win first21:49 – Taylor Swift tickets vs. switching your utility provider26:15 – Marketing to agents, not people28:43 – Fraud, inclusion, and vulnerable consumers32:38 – Liability: why there's no single framework44:18 – Who will actually build these agents49:25 – The one piece of advice for every fintech founder👉 Connect with Lucy and Anatole:LinkedIn: https://www.linkedin.com/in/anatole-baboukhian/LinkedIn: https://www.linkedin.com/in/lucykanderson/👉 Connect with Monica:LinkedIn: https://www.linkedin.com/in/monicamillares/Website: https://fintech-product-ai-lab.lovable.app/Disclaimer: This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion in social media.

João Moura built a fraud detection platform operating in 188 countries not by making fraud prevention safer, but by turning it into a growth lever. This conversation gets into how organized crime runs modern fraud, why siloed data guarantees you'll lose, and how connecting institutions across borders changes everything.What you'll learnWhy 70% of fraud today is backed by organized crime networks, and what that means for your detection strategy (02:46)How mule accounts operate as layered networks across institutions and why catching one account misses the point (32:20)What false declines actually cost you in revenue, customer trust, and operational overhead (09:03)How fraud detection becomes a growth enabler when it lets acquirers onboard merchants same-day with confidence (20:19)Why connecting data across institutions creates a network effect that makes every client's fraud model stronger (28:40)What the third generation of AI-powered fraud detection looks like and why it's fundamentally different from siloed models (14:45)How agentic AI introduces a new authorization problem that nobody in payments has solved yet (37:15)What João learned about distribution, decisiveness, and sunk cost fallacies after spinning Fraudio out of a bank (46:20)TIMESTAMPS1:35 João's background: 20 years in AI before it was cool2:46 How organized crime powers modern fraud7:55 The real cost of false declines on revenue and customers14:45 Three generations of AI fraud detection explained16:34 How generative AI acts as a force multiplier for fraud teams20:19 Detecting fraudulent merchants before chargebacks arrive25:13 How much fraud Fraudio actually stops: the numbers28:40 The Philippines rule that becomes available to 188 countries32:20 Why mule accounts are really mule networks37:15 Agentic AI and the unresolved authorization problem43:30 The lazy insight that became Fraudio46:20 What João learned: distribution, decisiveness, and sunk cost fallacies👉 Connect with João:LinkedIn: https://www.linkedin.com/in/mourajoaoWebsite: https://www.fraudio.com/👉 Connect with Monica:LinkedIn: https://www.linkedin.com/in/monicamillares/Website: https://fintech-product-ai-lab.lovable.app/Disclaimer: This episode does not constitute professional nor financial advice and does not represent the opinion nor views of my current, past or future employers. The guest has agreed to record and release our conversation for the use of this podcast and promotion in social media.