Kelvin Teo

speaker

586 appearances 1 recordings 1 series first heard Mar 2026 last heard 15 Mar

Kelvin Teo’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Mar 2026 with 1.

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BRAVE Southeast Asia Tech: Singapore, Indonesia, Vietnam, Philippines, Thailand & Malaysia Startups, Founders & Venture Capital VC (English) · How Kelvin Teo Built Southeast Asia’s Largest SME FinTech Empire - E678 · 15 Mar 2026 podcast

Because the one funny story was that Sequoia actually asked us after our sit round, Sequoia reached out to us, they were asking us questions.

I didn't know that's called due diligence at that point in time.

I was just being obliging that your Sequoia asked me questions.

Or answer, right?

So they asked me questions, I answered, they answered for a meeting.

We've we flew back, attended a meeting, and then we went and then flew to Boston again within 72 hours, right?

Uh so we didn't know that that was quite an Icy meeting.

Um so they offered a term sheet and then subsequently put back weeks later and it was like, dude, you ask how can I do something that's such bad form?

Um.

And it turns out that they didn't know that we were students.

We're just looking at Asian hours at that point in time.

But very honoured that they waited for us upon right before graduation they gave us a termship again.

And they've been there with for us during various shocks and whatnot, uh, which also some of them related to the credit for the market also.

Yeah.

So so I think why they invested in us is that they said we were very aggressive when it comes to execution, but very conservative when it comes to risk management as well as regulations, right?

So when others were just, hey, let's just go and show skills so that we can actually raise bigger capital, we took the opposite approach that hey, we will go steadily um to make sure that we have a quality

book and whatnot, right?

So so that was that was how we've started.

So it was in our DNA that we want to be careful and whatnot.

It's also partly why there's this funny story that in the first five years we barely have enough default data to train the model enough because there were there were quite few of them, right?

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