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 learn
Who the 60 million credit cardless Brazilians actually are
Why adding a local payment method can grow your addressable market instead of cannibalizing card revenue
How mandated adoption made Pix stick where other countries' instant payment schemes stalled
Who actually funds the 3 to 5% discount merchants give you for paying with Pix, and why they choose to
The go-to-market that took EBANX to 38% of Pix Automático transactions
Why card subscriptions lose 20 to 30% of recurring revenue to churn, and how a recurring QR rail claws it back
How B2B and SaaS billing is quietly moving onto a rail built for consumers
Why you can win a customer with marketing and still lose them at the payment checkout

Timestamps
00:00 — 60 million people, no credit card
02:26 — Who actually can't get a credit card in Brazil
04:37 — How EBANX connects global merchants to local payers
06:14 — Pix versus Pix Automático, and why recurring QR matters
09:17 — Is Pix Automático cannibalizing cards, or growing the market?
13:05 — Why Pix succeeded where other countries' schemes stalled
21:33 — Who funds the Pix discounts
23:50 — The go-to-market behind 38% market share
29:36 — Why the checkout screen decides conversion
35:01 — B2B and SaaS are paying by QR
39:24 — Reducing involuntary churn with Pix Automático
43:46 — Expanding into Southeast Asia and handling the FX
👉Connect with Sebastian:
EBANX: https://www.ebanx.com
LinkedIn: https://www.linkedin.com/in/sebastian-fantini-a0a1b970/
👉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/videos
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 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 identity
Why one signal only creates suspicion, and how voice, behavior, device and transaction history together are what create confidence
Why fraud detection has to happen during the call, not after the money has moved
Why community banks and credit unions carry the same threat as the largest banks with almost none of the resources
When it makes sense to partner rather than build fraud infrastructure in-house
What "explainable" fraud AI actually means to a regulator, and why a risk score on its own no longer holds up
What fraud looks like once AI makes it cheap enough for anyone to run, and why trust infrastructure has to come first
How Anat found and validated the idea through customer discovery, and what she means by resilience built one decision at a time

Timestamps
00:00 — 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 time
27:50 — When fraud gets cheap: good AI versus bad AI
Connect 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/videos
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.

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 learn
Why carbon offsetting and "your carbon footprint" features fail to change customer behavior, and what the psychology actually is
The handprint model: quantifying positive impact instead of negative impact, and why that flips the entire customer relationship
How banks turn nature engagement into a growth engine: referral programs with co-owned impact, nature cashback instead of standard cashback
Why 62% of customers would switch banks over purpose misalignment, and 52% already have
The phoenix metric: measuring company success in ecosystem value generated, not just valuation
Mathias's biggest founder mistakes, and the best sales advice he's ever received: "implicate the pain"

Timestamps
00:00 — Cold open: has carbon offsetting failed?
04:40 — What Handprint actually does
06:55 — Phoenix vs. unicorn: a new metric for success
09:00 — $23 million in ecosystem value, every year
18:30 — Why donation and carbon-guilt features don't work
24:40 — Kill the cashback war: nature cashback instead
27:30 — From cost center to growth engine: the 875% number
32:30 — Building a company from Bali
35:30 — Every mistake we could have made, we've made
41:35 — Implicate the pain: the best sales advice he's received
44:00 — Resilience, mindset, and having fun in the pain

Connect 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/videos

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.

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 autonomous
KYB still isn't solved. KYA (Know Your Agent) is next, and it's harder: identity, mandate, scope
Liability isn't one framework: it depends where in the chain the failure happens
Boring, 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 people

TIMESTAMPS
00:00 – What agentic commerce actually means
01:27 – The two use cases nobody separates
07:19 – What regulators are really asking
09:32 – KYC → KYB → KYA: the trust gap nobody's closed
16:42 – The three-layer KYA framework: identity, mandate, scope
18:44 – Why the "boring" use cases will win first
21:49 – Taylor Swift tickets vs. switching your utility provider
26:15 – Marketing to agents, not people
28:43 – Fraud, inclusion, and vulnerable consumers
32:38 – Liability: why there's no single framework
44:18 – Who will actually build these agents
49:25 – 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 learn
Why 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)

TIMESTAMPS
1:35 João's background: 20 years in AI before it was cool
2:46 How organized crime powers modern fraud
7:55 The real cost of false declines on revenue and customers
14:45 Three generations of AI fraud detection explained
16:34 How generative AI acts as a force multiplier for fraud teams
20:19 Detecting fraudulent merchants before chargebacks arrive
25:13 How much fraud Fraudio actually stops: the numbers
28:40 The Philippines rule that becomes available to 188 countries
32:20 Why mule accounts are really mule networks
37:15 Agentic AI and the unresolved authorization problem
43:30 The lazy insight that became Fraudio
46:20 What João learned: distribution, decisiveness, and sunk cost fallacies

👉 Connect with João:
LinkedIn: https://www.linkedin.com/in/mourajoao
Website: 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.