Aug 14, 2026 · 32 min · 10 segments
After 17 years in the British Army, Wayne Hennessy-Barrett made an unlikely move into African fintech. Today he leads 4G Capital, a fintech lender that provides millions of working capital loans to…
Wayne Hennessy-BarrettGuest
Chipo MuwowoHost
I'm intrigued by how do you protect your downside? I'm conscious that these are relatively very small loans so you know if one could argue that actually it's from your perspective it's fine you know they're not massive massive loans but you do have to protect yourself somehow so how does that all fit into this whole
operation sure sure so so the whole thesis it's not straightforward it's not easy i mean certainly startup fundraising conversations i'd I'd have a lot of people tell me about the low barriers of entry to my sector.
It's getting repaid and getting repaid consistently and getting repaid for the right reasons because the customer wishes to repay because they then want to access another loan because you're helping their business grow.
So we apply at least retail bank levels of due diligence and know your client probably a little bit higher if I'm honest.
We make sure that we lend to the right people for the right purpose with the right product.
So we make sure that we're lending to a viable business that's going to be able to repay that loan and make money with it before we lend.
And we've got a very smart algorithm, EVA, our evaluation algorithm, because every AI needs a name.
And with a relatively small amount of data, we can get a very, very good read on what a business needs to be able to buy inventory, sell it at a good markup, repay us, bank the surplus they've made so that they are up at the end of the cycle.
And so from pretty small acorns, we're now lending, we'll probably lend just under $400 million this year.
We'll be getting to our $1 billion total dispersed milestone in the next couple of months.
But what matters is making sure that customer succeeds by delivering that loan on the right terms for them, which I've already spoken about.
So we're very proud of the fact that our customers will grow their revenues by an average of 82% year on year.

I'm intrigued by how do you protect your downside? I'm conscious that these are relatively very small loans so you know if one could argue that actually it's from your perspective it's fine you know they're not massive massive loans but you do have to protect yourself somehow so how does that all fit into this whole
operation sure sure so so the whole thesis it's not straightforward it's not easy i mean certainly startup fundraising conversations i'd I'd have a lot of people tell me about the low barriers of entry to my sector.
It's getting repaid and getting repaid consistently and getting repaid for the right reasons because the customer wishes to repay because they then want to access another loan because you're helping their business grow.
So we apply at least retail bank levels of due diligence and know your client probably a little bit higher if I'm honest.
We make sure that we lend to the right people for the right purpose with the right product.
So we make sure that we're lending to a viable business that's going to be able to repay that loan and make money with it before we lend.
And we've got a very smart algorithm, EVA, our evaluation algorithm, because every AI needs a name.
And with a relatively small amount of data, we can get a very, very good read on what a business needs to be able to buy inventory, sell it at a good markup, repay us, bank the surplus they've made so that they are up at the end of the cycle.
And so from pretty small acorns, we're now lending, we'll probably lend just under $400 million this year.
We'll be getting to our $1 billion total dispersed milestone in the next couple of months.
But what matters is making sure that customer succeeds by delivering that loan on the right terms for them, which I've already spoken about.
So we're very proud of the fact that our customers will grow their revenues by an average of 82% year on year.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.