And I said, come on, what are you talking about? Everybody in Mexico has a mobile phone and in the region is the same, the same scenario.

So at the end, they are producing the little prints, I will say every second every minute of their day. So with alternative data, now they have the opportunity to go after this population.

Each time where I think that this cross border space between Brazil and the world has reached its maximum I'm wrong: every year it just keeps growing steadily and steadily and steadily.

79% of Brazilian consumers typically would divide their purchases into instalments. So this is a reality has been a reality. Now, we are also integrated with NuPay, which offers the user the capability to make a purchase with an offshore motion, and have the interest rate defined by no bank itself in the checkout process.

What we're focused on is to make the financial products available for merchants that don't have a local entity and a local structure in Brazil, so that they can great for the consumer base the same experience that our local ecommerce player can provide.

It's another excellent question, because I think there's a lot of discussion about big data and AI and machine learning. And they go together well, big data and machine learning, but they're not the same thing.

A lot of what gets done with machine learning in our industry is applying very nonlinear methods to the same old data. In fact, everything I've talked about so far has been more intelligent use of the data you've always had, Building Better models of your business and of your product.

If you have unique data, that's great. And often we find unique datasets in finance companies, where they're doing some kind of specialty lending. You know, one of my favourites for a long time was a group that was looking at point of sale loans for cruise ship tickets.

when we are looking at new data sources, we have four acid tests that we run through.

The first is the reliability of the data - so is it from a trustworthy source? Is it compliant? Is it of good quality, all those sorts of factors. The second, as you said, is the predictive nature of the data, is it proving out or identifying the hypotheses in the insights that we expected and is doing something which existing datasets can't? The third one is about its scalability, which doesn't mean it has to be the whole population, but for the actual target audience that we're trying to benefit, does it have enough coverage? And the fourth one is, is it understandable, which I think is increasingly important for us.

And we deliberately set the bar understandable rather than explainable because they're different. My science teacher, much to his frustration, spent hours explaining the basics of physics to me, unfortunately, for me, it wasn't very understandable. But actually, that is a really that's a really important point, because it comes back to this control and transparency. If people do not understand it, then why would I trust it? So for us as organisations with new data and new insights, then we have to be able to help people understand why we're doing it and, and the inferences we're drawing.

"The risk of giving into temptation is as old as humanity. But there are reasons to think that people today are having to work harder to resist it, particularly when it comes to consumer behaviour. Digital technology has made it easier and faster to buy goods and services in an instant, without the delays of processing that once comprised an inbuilt cooling off period". This might sound like a headline from today's papers, but in fact it was from an article in The Financial Times published seven years ago, almost to the day - at a time when Klarna was around, yes, but only just beginning its global expansion, Affirm was only two years old, and AfterPay only a few months old. Welcome back to How to Lend Money to Strangers, the podcast about consumer lending strategies across the credit lifecycle and around the world.

There's a whole bunch of people out there where the traditional model doesn't work, there simply isn't enough information on these people to make a reasonable credit call...

This is a space where lots of people are working, but very few people can claim results. Because this is also the sort of space where lots of AI propellerheads think they can crack the problem. To some extent, that's true. This is also the classic case where progress is both hindered and aided with experience. It's actually good that some youngster on a beanbag, with long hair, thinks about this stuff completely unhindered by any previous industry knowledge, because that's anyone with too much experience probably thinks too much inside the box. At the same time, with something like credit, you do need to have some other people in there who can say, 'well, yeah, that's cool but you need to take these following five things in'.

That doesn't mean that the thinking needs to be restrained, but someone needs to make it practical in the end. To simply let the same space cadets go mad on this is likely to land you in a heap of problems, if you don't actually understand the lending industry.